Visualisation of Temporal Interval Association Rules
|
|
- Julius Montgomery
- 5 years ago
- Views:
Transcription
1 Visualisation of Temporal Interval Association Rules Chris P. Rainsford 1 and John F. Roddick 2 1 Defence Science and Technology Organisation, DSTO C3 Research Centre Fernhill Park, Canberra, 2600, Australia. chris.rainsford@dsto.defence.gov.au 2 School of Informatics and Engineering, Flinders University of South Australia GPO Box 2100, Adelaide 5001, Australia. roddick@cs.flinders.edu.au Abstract. Temporal intervals and the interaction of interval-based events are fundamental in many domains including medicine, commerce, computer security and various types of normalcy analysis. In order to learn from temporal interval data we have developed a temporal interval association rule algorithm. In this paper, we will provide a definition for temporal interval association rules and present our visualisation techniques for viewing them. Visualisation techniques are particularly important because the complexity and volume of knowledge that is discovered during data mining often makes it difficult to comprehend. We adopt a circular graph for visualising a set of associations that allows underlying patterns in the associations to be identified. To visualize temporal relationships, a parallel coordinate graph for displaying the temporal relationships has been developed. 1 Introduction In recent years data mining has emerged as a field of investigation concerned with automating the process of finding patterns within large volumes of data [9]. The results of data mining are often complex in their own right and visualisation has been widely employed as a technique for assisting users in seeing the underlying semantics [12]. In addition, mining from temporal data has received increased attention recently as it provides insight into the nature of changes in data [11]. Temporal intervals are inherent in nature and in many business domains that are modelled within information systems. In order to capture these semantics, we have developed an extension to the definition of association rules [1] to accommodate temporal interval data [10]. Association rules have been widely used as a data mining tool for market analysis, inference in medical data and product promotion. By extending these rules to accommodate temporal intervals, we allow users to find patterns that describe the interaction between events and intervals over time. For example, a financial services company may be interested to see the way in which certain products and portfolios are interrelated. Customers may initially purchase an insurance policy and then open an investment portfolio or superannuation fund with the same company. It may then be interesting to see which the customer terminates first. Likewise, a customer history may show that they have held investments in three
2 different investment funds. It may then be interesting to see if all three were held simultaneously, one following the other, or in some overlapping fashion. Looking for underlying trends and patterns in this type of behaviour is likely to be highly useful for analysts who are seeking to market these products, both to new investors and long term clients. In order to increase the comprehensibility of rules that describe such relationships, we have also developed two visualisation tools. The first tool uses a circular graph to display the underlying association rules. This allows the user to see patterns within the underlying associations. The second visualisation uses a parallel coordinate approach to present the temporal relationships that exist within the data in an easily comprehendible format. Importantly, both of these visualisation techniques are capable of displaying large numbers of rules and can be easily represented in a fixed two-dimensional format that can be easily reproduced on paper or other media. In the next section we will provide a definition for temporal interval association rules. Section 3 discusses our association rule visualisation tool. Section 4 then describes our temporal relationship visualiser. A conclusion is provided in Section 5. 2 Temporal Interval Association Rules We define a temporal interval association rule to be a conventional association rule that includes a conjunction of one or more temporal relationships between items in the antecedent or consequent. Building upon the original formalism in [1] temporal interval association rules can be defined as follows: Let I = I 1, I 2,...,I m be a set of binary attributes or items and T be a database of tuples. Association rules were first proposed for use within transaction databases, where each transaction t is recorded with a corresponding tuple. Hence attributes represented items and were limited to a binary domain where t(k) = 1 indicated that the item I k was positive in that case (for example, had been purchased as part of the transaction, observed in that individual, etc.), and t(k) = 0 indicated that it had not. Temporal attributes are defined as attributes with associated temporal points or intervals that record the time for which the item or attribute was valid in the modeled domain. Let X be a set of some attributes in I. It can be said that a transaction t satisfies X if, for all attributes I k in X, t(k) = 1. Consider a conjunction of binary temporal predicates P 1 P n defined on attributes contained in either X or Y where n 0. Then by a temporal association rule, we mean an implication of the form X Y P 1... P n, where X, the antecedent, is a set of attributes in I and Y, the consequent, is a set of attributes in I that are not present in X. The rule X Y P 1... P n is satisfied in the set of transactions T with the confidence factor 0 c 1 iff at least c% of transactions in T that satisfy X also satisfy Y. Likewise each predicate P i is satisfied with a temporal confidence factor of 0 tc Pi 1 iff at least tc% of transactions in T that satisfy X and Y also satisfy P i. The notation X Y c P 1 tc tc P n tc is adopted to specify that the rule X Y P 1 P n has a confidence factor of c and temporal confidence factor of tc. As an illustration consider the following simple example rule: policyz investx,producty 0.79 during(investx,policyz) 0.75 before(producty,investx) 0.81
3 This rule can be read as follows: The purchase of investment X and product Y are associated with insurance policy Z with a confidence factor of The investment in X occurs during the period of policy Z with a temporal confidence factor of 0.75 and the purchase of product Y occurs before investment X with a temporal confidence factor of 0.81 Binary temporal predicates are defined using Allen s thirteen interval-based relationships between two intervals. A thorough description of these relationships can be found in [2]. We also use the neighborhood relationships defined by Freksa that allow generalisation of relationships [5]. A detailed description of our learning algorithm is beyond the scope of this paper and readers are referred to [10]. 3 Visualising Associations Finding patterns within the temporal interval associations may be assisted with the use of visualisation techniques. This is particularly important where the number of association rules is found to be large and the discovery of underlying patterns by inspection is not possible. For this purpose we have devised two separate visualisation techniques. The first can be used to visualise any association rule and the second is specific to temporal associations. The visualisation of sets of association rules has been addressed in a number of different ways. One approach has been to draw connected graphs [6]. However, if the number of rules is large this approach involves a complex layout process that needs to be optimised in order to avoid cluttering the graph. An elegant threedimensional model is provided in the MineSet software tool [4]. We have chosen to develop a visualisation that can handle a large volume of associations and that can be easily reproduced in two-dimensions, e.g. as a paper document, or an overhead projection slide. In addition, it provides an at-a-glance view of the data that does not need to be navigated and explored to be fully understood. This approach complements the approaches of others and is more applicable in some circumstances. We have adopted a circular graph layout where items involved in rules are mapped around the circumference of a circle, see Figure 1. Associations are then plotted as lines connecting these points, where a gradient in the colour of the line, from blue(dark) to yellow(light) indicates the direction of the association from the antecedent to the consequent. A green line highlights associations that are bidirectional and this allows bi-directional relationships to be immediately identified. Circular graph layouts have been successfully used in several other data mining applications, including Netmap [3],[8]. A key characteristic of this type of visualization is its ability to display large volumes of information. The circle graph gives an intuitive feel for patterns within the underlying data. For example, items that have several other items associated with them will have a number of blue lines leaving their node on the circle. These items may be selected for marketing to attract new clients, because it is likely that the clients will also purchase other items or services as part of an overall basket. Note however that no temporal information is
4 provided in this graph. In cases where the number of items is large, concept ascension may be employed to reduce complexity. Fig. 1. A screenshot of our association rule visualisation window 4 Visualising Temporal Associations Our first visualisation does not display the details of discovered temporal relationships between items in the association rules. In order to display this information it has been necessary to develop a new visualization tool. We have developed a simple visualisation technique based upon parallel coordinate visualisation. Parallel coordinate visualization has been used successfully in other data mining tools to display large volumes of data [7]. A screenshot of this visualisation is depicted in Figure 2. We start by plotting all of the items on the righthand side of temporal predicates along a vertical axis. The items on the left-hand side of the temporal predicate are plotted along an axis on the opposite side of the screen with the labels for the thirty temporal relationships we have adopted lined along a central vertical axis. The temporal relationships can be seen as semi-ordered based
5 upon the location of two intervals with respect to each other along the time axis. Using simple heuristics we have imposed an artificial ordering upon the relationships in order to allow them to be represented meaningfully along a single line. We then draw lines between items that have a temporal relationship and the lines intersect the central axis at the point that corresponds to the nature of that relationship. The lines are coloured to reflect the temporal confidence associated with the underlying relationship. Fig. 2. A screenshot of our temporal interval visualisation window. Based upon this visualisation it is possible to quickly determine patterns within the data. For example, a financial services company may seek to identify marketing opportunities for items to its current clients. By looking for items on the right-hand side of the graph, that are connected via lines that run predominately through the top half of the temporal relationship line (corresponding to items purchased after the item on the left hand side). The market analyst may then seek to market these services to holders of the connected items on the left-hand side of the graph. The strongest such correlations can be identified based upon the colour of the line which indicates the confidence of the relationship. The colour of these lines can be observed to quickly estimate the strength of relationships.
6 5 Summary In this paper we have detailed two visualisation techniques to support the analysis of temporal interval association rules. These techniques are designed to allow a rapid understanding of patterns existing within large sets of rules. The first technique is a circular association rule graph that displays patterns within association rules. The second technique is based upon a parallel coordinate visualisation and it displays the temporal interval relationships between items. Both of these techniques have been successfully used for other data mining applications. Importantly, they are able to handle high volumes of data in a way that still allows users to find underlying patterns. These two techniques are simple and can be represented in two dimensions so that they can be easily reproduced. Research at both DSTO and at Flinders University is continuing and we plan to further refine these techniques and to examine their scalability to larger datasets. References 1. Agrawal, A., Imielinski, T., Swami, A. Mining Association Rules between Sets of Items in Large Databases. International Conference on Management of Data (SIGMOD 93), May (1993) Allen, J. F.: Maintaining knowledge about temporal intervals. Communications of the ACM Vol 26. No.11 (1983). 3. Aumann Y., Feldman R., Yehuda Y.B., Landau D., Liphstat O., Schler Y. Circle Graphs: New Visualization Tools for Text-Mining. in The 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, (PKDD-99). Prague, Czech Republic, September (1999). 4. Brunk, C. K., J. Kohavi, R. MineSet An Integrated System for Data Mining. Third International Conference on Knowledge Discovery and Data Mining (KDD 97), Newport Beach, California, AAAI Press. August 14-17, (1997) Freksa, C. Temporal reasoning based on semi-intervals. Artificial Intelligence 54, (1992) Klemettinen, M., Mannila H., Ronkainen, P., Toivonen H., Verkamo, A.I. Finding interesting Rules from Large Sets of Discovered Association Rules. Third International Conference on Information and Knowledge Management, Gaithersburg, Maryland, ACM Press. (1994). 7. Lee, H., Ong, H., Sodhi, K.S. Visual Data Exploration. The 3rd International Applied Statistics Conference, Dallas, Texas.(1995). 8. Netmap Technical Manual. The Technical Manual for the Netmap System. Netmap Solutions Pty Ltd, North Sydney NSW, Australia. (1994). 9. Piatetsky-Shapiro, G. and W. Frawley, J., Eds. Knowledge Discovery in Databases. Menlo park, California, AAAI Press, (1991). 10. Rainsford C.P., Roddick J.F. Adding Temporal Semantics to Association Rules. in The 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, (PKDD-99). Prague, Czech Republic, September (1999). 11. Roddick, J. F. and M. Spiliopoulou. A Survey of Temporal Knowledge Discovery Paradigms and Methods. IEEE Transactions on Knowledge and Data Engineering, to appear. (2000). 12. Tattersall, G. D. and P. R. Limb. Visulisation Techniques for Data Mining., BT Technol Journal 12(4).(1994).
Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach
Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach Gediminas Adomavicius Computer Science Department Alexander Tuzhilin Leonard N. Stern School of Business Workinq Paper Series
More informationA Data Mining Framework for Extracting Product Sales Patterns in Retail Store Transactions Using Association Rules: A Case Study
A Data Mining Framework for Extracting Product Sales Patterns in Retail Store Transactions Using Association Rules: A Case Study Mirzaei.Afshin 1, Sheikh.Reza 2 1 Department of Industrial Engineering and
More informationVisualization Techniques to Explore Data Mining Results for Document Collections
Visualization Techniques to Explore Data Mining Results for Document Collections Ronen Feldman Math and Computer Science Department Bar-Ilan University Ramat-Gan, ISRAEL 52900 feldman@macs.biu.ac.il Willi
More informationMetaData for Database Mining
MetaData for Database Mining John Cleary, Geoffrey Holmes, Sally Jo Cunningham, and Ian H. Witten Department of Computer Science University of Waikato Hamilton, New Zealand. Abstract: At present, a machine
More informationDiscovering interesting rules from financial data
Discovering interesting rules from financial data Przemysław Sołdacki Institute of Computer Science Warsaw University of Technology Ul. Andersa 13, 00-159 Warszawa Tel: +48 609129896 email: psoldack@ii.pw.edu.pl
More informationAn Evolutionary Algorithm for Mining Association Rules Using Boolean Approach
An Evolutionary Algorithm for Mining Association Rules Using Boolean Approach ABSTRACT G.Ravi Kumar 1 Dr.G.A. Ramachandra 2 G.Sunitha 3 1. Research Scholar, Department of Computer Science &Technology,
More informationDiscovery of Association Rules in Temporal Databases 1
Discovery of Association Rules in Temporal Databases 1 Abdullah Uz Tansel 2 and Necip Fazil Ayan Department of Computer Engineering and Information Science Bilkent University 06533, Ankara, Turkey {atansel,
More informationValue Added Association Rules
Value Added Association Rules T.Y. Lin San Jose State University drlin@sjsu.edu Glossary Association Rule Mining A Association Rule Mining is an exploratory learning task to discover some hidden, dependency
More informationImproved Frequent Pattern Mining Algorithm with Indexing
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 6, Ver. VII (Nov Dec. 2014), PP 73-78 Improved Frequent Pattern Mining Algorithm with Indexing Prof.
More informationCircle Graphs: New Visualization Tools for Text-Mining
Circle Graphs: New Visualization Tools for Text-Mining Yonatan Aumann, Ronen Feldman, Yaron Ben Yehuda, David Landau, Orly Liphstat, Yonatan Schler Department of Mathematics and Computer Science Bar-Ilan
More informationPerformance Based Study of Association Rule Algorithms On Voter DB
Performance Based Study of Association Rule Algorithms On Voter DB K.Padmavathi 1, R.Aruna Kirithika 2 1 Department of BCA, St.Joseph s College, Thiruvalluvar University, Cuddalore, Tamil Nadu, India,
More informationA mining method for tracking changes in temporal association rules from an encoded database
A mining method for tracking changes in temporal association rules from an encoded database Chelliah Balasubramanian *, Karuppaswamy Duraiswamy ** K.S.Rangasamy College of Technology, Tiruchengode, Tamil
More informationDIVERSITY-BASED INTERESTINGNESS MEASURES FOR ASSOCIATION RULE MINING
DIVERSITY-BASED INTERESTINGNESS MEASURES FOR ASSOCIATION RULE MINING Huebner, Richard A. Norwich University rhuebner@norwich.edu ABSTRACT Association rule interestingness measures are used to help select
More informationFinding Local and Periodic Association Rules from Fuzzy Temporal Data
Finding Local and Periodic Association Rules from Fuzzy Temporal Data F. A. Mazarbhuiya, M. Shenify, Md. Husamuddin College of Computer Science and IT Albaha University, Albaha, KSA fokrul_2005@yahoo.com
More informationCOLLABORATIVE AGENT LEARNING USING HYBRID NEUROCOMPUTING
COLLABORATIVE AGENT LEARNING USING HYBRID NEUROCOMPUTING Saulat Farooque and Lakhmi Jain School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia saulat.farooque@tenix.com,
More informationDiscovering Periodic Patterns in System Logs
Discovering Periodic Patterns in System Logs Marcin Zimniak 1, Janusz R. Getta 2, and Wolfgang Benn 1 1 Faculty of Computer Science, TU Chemnitz, Germany {marcin.zimniak,benn}@cs.tu-chemnitz.de 2 School
More informationTo Enhance Projection Scalability of Item Transactions by Parallel and Partition Projection using Dynamic Data Set
To Enhance Scalability of Item Transactions by Parallel and Partition using Dynamic Data Set Priyanka Soni, Research Scholar (CSE), MTRI, Bhopal, priyanka.soni379@gmail.com Dhirendra Kumar Jha, MTRI, Bhopal,
More informationDatabase and Knowledge-Base Systems: Data Mining. Martin Ester
Database and Knowledge-Base Systems: Data Mining Martin Ester Simon Fraser University School of Computing Science Graduate Course Spring 2006 CMPT 843, SFU, Martin Ester, 1-06 1 Introduction [Fayyad, Piatetsky-Shapiro
More informationThe Discovery and Retrieval of Temporal Rules in Interval Sequence Data
The Discovery and Retrieval of Temporal Rules in Interval Sequence Data by Edi Winarko, B.Sc., M.Sc. School of Informatics and Engineering, Faculty of Science and Engineering March 19, 2007 A thesis presented
More informationMining Generalised Emerging Patterns
Mining Generalised Emerging Patterns Xiaoyuan Qian, James Bailey, Christopher Leckie Department of Computer Science and Software Engineering University of Melbourne, Australia {jbailey, caleckie}@csse.unimelb.edu.au
More informationMining Negative Rules using GRD
Mining Negative Rules using GRD D. R. Thiruvady and G. I. Webb School of Computer Science and Software Engineering, Monash University, Wellington Road, Clayton, Victoria 3800 Australia, Dhananjay Thiruvady@hotmail.com,
More informationInternational Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May Dr.Zakea Il-Agure and Mr.Hicham Noureddine Itani
LINK MINING PROCESS Dr.Zakea Il-Agure and Mr.Hicham Noureddine Itani Higher Colleges of Technology, United Arab Emirates ABSTRACT Many data mining and knowledge discovery methodologies and process models
More informationUsability Evaluation as a Component of the OPEN Development Framework
Usability Evaluation as a Component of the OPEN Development Framework John Eklund Access Testing Centre and The University of Sydney 112 Alexander Street, Crows Nest NSW 2065 Australia johne@testingcentre.com
More informationTadeusz Morzy, Maciej Zakrzewicz
From: KDD-98 Proceedings. Copyright 998, AAAI (www.aaai.org). All rights reserved. Group Bitmap Index: A Structure for Association Rules Retrieval Tadeusz Morzy, Maciej Zakrzewicz Institute of Computing
More informationA Statistical Approach to Rule Selection in Semantic Query Optimisation
A Statistical Approach to Rule Selection in Semantic Query Optimisation Barry G. T. Lowden and Jerome Robinson Department of Computer Science, The University of ssex, Wivenhoe Park, Colchester, CO4 3SQ,
More informationUsing EasyMiner API for Financial Data Analysis in the OpenBudgets.eu Project
Using EasyMiner API for Financial Data Analysis in the OpenBudgets.eu Project Stanislav Vojíř 1, Václav Zeman 1, Jaroslav Kuchař 1,2, Tomáš Kliegr 1 1 University of Economics, Prague, nám. W Churchilla
More informationDATABASE ISSUES IN KNOWLEDGE DISCOVERY AND DATA MINING ABSTRACT INTRODUCTION
DATABASE ISSUES IN KNOWLEDGE DISCOVERY AND DATA MINING Chris P. Rainsford Defence Science and Technology Organisation Information Technology Division DSTO C3 Research Centre Femhill Park, Canberra 2600
More informationInduction of Association Rules: Apriori Implementation
1 Induction of Association Rules: Apriori Implementation Christian Borgelt and Rudolf Kruse Department of Knowledge Processing and Language Engineering School of Computer Science Otto-von-Guericke-University
More informationMining Temporal Association Rules in Network Traffic Data
Mining Temporal Association Rules in Network Traffic Data Guojun Mao Abstract Mining association rules is one of the most important and popular task in data mining. Current researches focus on discovering
More informationAssociation Rule Selection in a Data Mining Environment
Association Rule Selection in a Data Mining Environment Mika Klemettinen 1, Heikki Mannila 2, and A. Inkeri Verkamo 1 1 University of Helsinki, Department of Computer Science P.O. Box 26, FIN 00014 University
More informationBachelor of Applied Finance (Financial Planning)
Course information for Bachelor of Applied Finance (Financial Planning) Course Number HE20521 Location St George Ultimo Course Structure The structure below is the typical study pattern for a full time
More informationMining of Web Server Logs using Extended Apriori Algorithm
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational
More informationPerformance Analysis of Frequent Closed Itemset Mining: PEPP Scalability over CHARM, CLOSET+ and BIDE
Volume 3, No. 1, Jan-Feb 2012 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info ISSN No. 0976-5697 Performance Analysis of Frequent Closed
More informationTEMPORAL data mining is a research field of growing
An Optimal Temporal and Feature Space Allocation in Supervised Data Mining S. Tom Au, Guangqin Ma, and Rensheng Wang, Abstract This paper presents an expository study of temporal data mining for prediction
More informationData Access Paths for Frequent Itemsets Discovery
Data Access Paths for Frequent Itemsets Discovery Marek Wojciechowski, Maciej Zakrzewicz Poznan University of Technology Institute of Computing Science {marekw, mzakrz}@cs.put.poznan.pl Abstract. A number
More informationAN IMPROVISED FREQUENT PATTERN TREE BASED ASSOCIATION RULE MINING TECHNIQUE WITH MINING FREQUENT ITEM SETS ALGORITHM AND A MODIFIED HEADER TABLE
AN IMPROVISED FREQUENT PATTERN TREE BASED ASSOCIATION RULE MINING TECHNIQUE WITH MINING FREQUENT ITEM SETS ALGORITHM AND A MODIFIED HEADER TABLE Vandit Agarwal 1, Mandhani Kushal 2 and Preetham Kumar 3
More informationGraph Based Approach for Finding Frequent Itemsets to Discover Association Rules
Graph Based Approach for Finding Frequent Itemsets to Discover Association Rules Manju Department of Computer Engg. CDL Govt. Polytechnic Education Society Nathusari Chopta, Sirsa Abstract The discovery
More informationA Literature Review of Modern Association Rule Mining Techniques
A Literature Review of Modern Association Rule Mining Techniques Rupa Rajoriya, Prof. Kailash Patidar Computer Science & engineering SSSIST Sehore, India rprajoriya21@gmail.com Abstract:-Data mining is
More informationTransforming Quantitative Transactional Databases into Binary Tables for Association Rule Mining Using the Apriori Algorithm
Transforming Quantitative Transactional Databases into Binary Tables for Association Rule Mining Using the Apriori Algorithm Expert Systems: Final (Research Paper) Project Daniel Josiah-Akintonde December
More informationOptimization using Ant Colony Algorithm
Optimization using Ant Colony Algorithm Er. Priya Batta 1, Er. Geetika Sharmai 2, Er. Deepshikha 3 1Faculty, Department of Computer Science, Chandigarh University,Gharaun,Mohali,Punjab 2Faculty, Department
More informationAssociation Rule Mining Using Revolution R for Market Basket Analysis
Association Rule Mining Using Revolution R for Market Basket Analysis Veepu Uppal 1, Dr.Rajesh Kumar Singh 2 1 Assistant Professor, Manav Rachna University, Faridabad, INDIA 2 Principal, Shaheed Udham
More information620 HUANG Liusheng, CHEN Huaping et al. Vol.15 this itemset. Itemsets that have minimum support (minsup) are called large itemsets, and all the others
Vol.15 No.6 J. Comput. Sci. & Technol. Nov. 2000 A Fast Algorithm for Mining Association Rules HUANG Liusheng (ΛΠ ), CHEN Huaping ( ±), WANG Xun (Φ Ψ) and CHEN Guoliang ( Ξ) National High Performance Computing
More informationAPD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data
APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data I-Hsien Ting, Lillian Clark, Chris Kimble, Daniel Kudenko, and Peter Wright Department of Computer Science, The University
More informationDiscovering the Association Rules in OLAP Data Cube with Daily Downloads of Folklore Materials *
Discovering the Association Rules in OLAP Data Cube with Daily Downloads of Folklore Materials * Galina Bogdanova, Tsvetanka Georgieva Abstract: Association rules mining is one kind of data mining techniques
More informationINTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET)
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 6367(Print) ISSN 0976 6375(Online)
More informationVisualisation of Abstract Information
Visualisation of Abstract Information Visualisation Lecture 17 Institute for Perception, Action & Behaviour School of Informatics Abstract Information 1 Information Visualisation Previously data with inherent
More informationASSOCIATION RULE MINING: MARKET BASKET ANALYSIS OF A GROCERY STORE
ASSOCIATION RULE MINING: MARKET BASKET ANALYSIS OF A GROCERY STORE Mustapha Muhammad Abubakar Dept. of computer Science & Engineering, Sharda University,Greater Noida, UP, (India) ABSTRACT Apriori algorithm
More informationCEM Visualisation and Discovery in
CEM Visualisation and Discovery in Email Richard Cole 1, Peter Eklund 1, Gerd Stumme 2 1 Knowledge, Visualisation and Ordering Laboratory School of Information Technology, Griffith University, Gold Coast
More informationMining the optimal class association rule set
Knowledge-Based Systems 15 (2002) 399 405 www.elsevier.com/locate/knosys Mining the optimal class association rule set Jiuyong Li a, *, Hong Shen b, Rodney Topor c a Dept. of Mathematics and Computing,
More informationAssociation Rule Learning
Association Rule Learning 16s1: COMP9417 Machine Learning and Data Mining School of Computer Science and Engineering, University of New South Wales March 15, 2016 COMP9417 ML & DM (CSE, UNSW) Association
More informationIJMIE Volume 2, Issue 9 ISSN:
WEB USAGE MINING: LEARNER CENTRIC APPROACH FOR E-BUSINESS APPLICATIONS B. NAVEENA DEVI* Abstract Emerging of web has put forward a great deal of challenges to web researchers for web based information
More informationPattern Mining. Knowledge Discovery and Data Mining 1. Roman Kern KTI, TU Graz. Roman Kern (KTI, TU Graz) Pattern Mining / 42
Pattern Mining Knowledge Discovery and Data Mining 1 Roman Kern KTI, TU Graz 2016-01-14 Roman Kern (KTI, TU Graz) Pattern Mining 2016-01-14 1 / 42 Outline 1 Introduction 2 Apriori Algorithm 3 FP-Growth
More informationAn Approach to Software Component Specification
Page 1 of 5 An Approach to Software Component Specification Jun Han Peninsula School of Computing and Information Technology Monash University, Melbourne, Australia Abstract. Current models for software
More informationTemporal Weighted Association Rule Mining for Classification
Temporal Weighted Association Rule Mining for Classification Purushottam Sharma and Kanak Saxena Abstract There are so many important techniques towards finding the association rules. But, when we consider
More informationrule mining can be used to analyze the share price R 1 : When the prices of IBM and SUN go up, at 80% same day.
Breaking the Barrier of Transactions: Mining Inter-Transaction Association Rules Anthony K. H. Tung 1 Hongjun Lu 2 Jiawei Han 1 Ling Feng 3 1 Simon Fraser University, British Columbia, Canada. fkhtung,hang@cs.sfu.ca
More informationMaintenance of Generalized Association Rules for Record Deletion Based on the Pre-Large Concept
Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and ata Bases, Corfu Island, Greece, February 16-19, 2007 142 Maintenance of Generalized Association Rules for
More informationSA-IFIM: Incrementally Mining Frequent Itemsets in Update Distorted Databases
SA-IFIM: Incrementally Mining Frequent Itemsets in Update Distorted Databases Jinlong Wang, Congfu Xu, Hongwei Dan, and Yunhe Pan Institute of Artificial Intelligence, Zhejiang University Hangzhou, 310027,
More informationAssociation-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications
Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications Daniel Mican, Nicolae Tomai Babes-Bolyai University, Dept. of Business Information Systems, Str. Theodor
More informationINTELLIGENT SUPERMARKET USING APRIORI
INTELLIGENT SUPERMARKET USING APRIORI Kasturi Medhekar 1, Arpita Mishra 2, Needhi Kore 3, Nilesh Dave 4 1,2,3,4Student, 3 rd year Diploma, Computer Engineering Department, Thakur Polytechnic, Mumbai, Maharashtra,
More informationDynamic Aggregation to Support Pattern Discovery: A case study with web logs
Dynamic Aggregation to Support Pattern Discovery: A case study with web logs Lida Tang and Ben Shneiderman Department of Computer Science University of Maryland College Park, MD 20720 {ltang, ben}@cs.umd.edu
More informationFeatured Articles AI Services and Platforms A Practical Approach to Increasing Business Sophistication
118 Hitachi Review Vol. 65 (2016), No. 6 Featured Articles AI Services and Platforms A Practical Approach to Increasing Business Sophistication Yasuharu Namba, Dr. Eng. Jun Yoshida Kazuaki Tokunaga Takuya
More informationDevelopment of Efficient & Optimized Algorithm for Knowledge Discovery in Spatial Database Systems
Development of Efficient & Optimized Algorithm for Knowledge Discovery in Spatial Database Systems Kapil AGGARWAL, India Key words: KDD, SDBS, neighborhood graph, neighborhood path, neighborhood index
More informationMixture models and frequent sets: combining global and local methods for 0 1 data
Mixture models and frequent sets: combining global and local methods for 1 data Jaakko Hollmén Jouni K. Seppänen Heikki Mannila Abstract We study the interaction between global and local techniques in
More informationReal World Performance of Association Rule Algorithms
To appear in KDD 2001 Real World Performance of Association Rule Algorithms Zijian Zheng Blue Martini Software 2600 Campus Drive San Mateo, CA 94403, USA +1 650 356 4223 zijian@bluemartini.com Ron Kohavi
More informationReducing Redundancy in Characteristic Rule Discovery by Using IP-Techniques
Reducing Redundancy in Characteristic Rule Discovery by Using IP-Techniques Tom Brijs, Koen Vanhoof and Geert Wets Limburg University Centre, Faculty of Applied Economic Sciences, B-3590 Diepenbeek, Belgium
More informationMining High Order Decision Rules
Mining High Order Decision Rules Y.Y. Yao Department of Computer Science, University of Regina Regina, Saskatchewan, Canada S4S 0A2 e-mail: yyao@cs.uregina.ca Abstract. We introduce the notion of high
More informationMining Association Rules with Item Constraints. Ramakrishnan Srikant and Quoc Vu and Rakesh Agrawal. IBM Almaden Research Center
Mining Association Rules with Item Constraints Ramakrishnan Srikant and Quoc Vu and Rakesh Agrawal IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120, U.S.A. fsrikant,qvu,ragrawalg@almaden.ibm.com
More informationMaterialized Data Mining Views *
Materialized Data Mining Views * Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz Poznan University of Technology Institute of Computing Science ul. Piotrowo 3a, 60-965 Poznan, Poland tel. +48 61
More informationCOLUMN. What attractive intranets look like. Intranets can t afford to be useful but ugly JULY Attractive and useful.
KM COLUMN JULY 2010 What attractive intranets look like The winds of change are blowing for intranets. Every intranet survey run in the wider community has shown that 50% of intranet teams are planning
More informationAn Approach to Intensional Query Answering at Multiple Abstraction Levels Using Data Mining Approaches
An Approach to Intensional Query Answering at Multiple Abstraction Levels Using Data Mining Approaches Suk-Chung Yoon E. K. Park Dept. of Computer Science Dept. of Software Architecture Widener University
More informationKeyword AAA. National Archives of Australia
Keyword AAA National Archives of Australia 1999 Commonwealth of Australia 1999 This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process
More informationPerformance Analysis of Data Mining Classification Techniques
Performance Analysis of Data Mining Classification Techniques Tejas Mehta 1, Dr. Dhaval Kathiriya 2 Ph.D. Student, School of Computer Science, Dr. Babasaheb Ambedkar Open University, Gujarat, India 1 Principal
More informationCourse on Data Mining ( )
Course on Data Mining (581550-4) Intro/Ass. Rules 24./26.10. Episodes 30.10. 7.11. Home Exam Clustering 14.11. KDD Process 21.11. Text Mining 28.11. Appl./Summary 21.11.2001 Data mining: KDD Process 1
More informationAssociation Rules Mining:References
Association Rules Mining:References Zhou Shuigeng March 26, 2006 AR Mining References 1 References: Frequent-pattern Mining Methods R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm
More informationInteractive Visualization of Fuzzy Set Operations
Interactive Visualization of Fuzzy Set Operations Yeseul Park* a, Jinah Park a a Computer Graphics and Visualization Laboratory, Korea Advanced Institute of Science and Technology, 119 Munji-ro, Yusung-gu,
More informationFundamentals of Design, Implementation, and Management Tenth Edition
Database Principles: Fundamentals of Design, Implementation, and Management Tenth Edition Chapter 3 Data Models Database Systems, 10th Edition 1 Objectives In this chapter, you will learn: About data modeling
More informationA Beginners Guide to UML Part II
A Beginners Guide to UML Part II Dan Brown, Dunstan Thomas Consulting Summary In the first part of this article, I examined the origins and definition of the UML to provide a basic understanding of what
More informationMining Frequent Patterns with Counting Inference at Multiple Levels
International Journal of Computer Applications (097 7) Volume 3 No.10, July 010 Mining Frequent Patterns with Counting Inference at Multiple Levels Mittar Vishav Deptt. Of IT M.M.University, Mullana Ruchika
More informationLocal Mining of Association Rules with Rule Schemas
Local Mining of Association Rules with Rule Schemas Andrei Olaru Claudia Marinica Fabrice Guillet LINA, Ecole Polytechnique de l'universite de Nantes rue Christian Pauc BP 50609 44306 Nantes Cedex 3 E-mail:
More informationFuzzy Cognitive Maps application for Webmining
Fuzzy Cognitive Maps application for Webmining Andreas Kakolyris Dept. Computer Science, University of Ioannina Greece, csst9942@otenet.gr George Stylios Dept. of Communications, Informatics and Management,
More informationMarwan AL-Abed Abu-Zanona Department of Computer Information System Jerash University Amman, Jordan
World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 1, No. 7, 311-316, 2011 An Improved Algorithm for Mining Association Rules in Large Databases Farah Hanna AL-Zawaidah
More informationMining Quantitative Association Rules on Overlapped Intervals
Mining Quantitative Association Rules on Overlapped Intervals Qiang Tong 1,3, Baoping Yan 2, and Yuanchun Zhou 1,3 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China {tongqiang,
More information2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media,
2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising
More informationA Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set
A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set Renu Vashist School of Computer Science and Engineering Shri Mata Vaishno Devi University, Katra,
More informationMaintenance of fast updated frequent pattern trees for record deletion
Maintenance of fast updated frequent pattern trees for record deletion Tzung-Pei Hong a,b,, Chun-Wei Lin c, Yu-Lung Wu d a Department of Computer Science and Information Engineering, National University
More informationUNCORRECTED PROOF ARTICLE IN PRESS. 1 Expert Systems with Applications. 2 Mining knowledge from object-oriented instances
1 Expert Systems with Applications Expert Systems with Applications xxx (26) xxx xxx wwwelseviercom/locate/eswa 2 Mining knowledge from object-oriented instances 3 Cheng-Ming Huang a, Tzung-Pei Hong b,
More informationStylus Studio Case Study: FIXML Working with Complex Message Sets Defined Using XML Schema
Stylus Studio Case Study: FIXML Working with Complex Message Sets Defined Using XML Schema Introduction The advanced XML Schema handling and presentation capabilities of Stylus Studio have valuable implications
More informationEfficient Remining of Generalized Multi-supported Association Rules under Support Update
Efficient Remining of Generalized Multi-supported Association Rules under Support Update WEN-YANG LIN 1 and MING-CHENG TSENG 1 Dept. of Information Management, Institute of Information Engineering I-Shou
More informationMining Temporal Indirect Associations
Mining Temporal Indirect Associations Ling Chen 1,2, Sourav S. Bhowmick 1, Jinyan Li 2 1 School of Computer Engineering, Nanyang Technological University, Singapore, 639798 2 Institute for Infocomm Research,
More informationOverview. Data-mining. Commercial & Scientific Applications. Ongoing Research Activities. From Research to Technology Transfer
Data Mining George Karypis Department of Computer Science Digital Technology Center University of Minnesota, Minneapolis, USA. http://www.cs.umn.edu/~karypis karypis@cs.umn.edu Overview Data-mining What
More informationInformation mining and information retrieval : methods and applications
Information mining and information retrieval : methods and applications J. Mothe, C. Chrisment Institut de Recherche en Informatique de Toulouse Université Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse
More informationApplying Objective Interestingness Measures. in Data Mining Systems. Robert J. Hilderman and Howard J. Hamilton. Department of Computer Science
Applying Objective Interestingness Measures in Data Mining Systems Robert J. Hilderman and Howard J. Hamilton Department of Computer Science University of Regina Regina, Saskatchewan, Canada SS 0A fhilder,hamiltong@cs.uregina.ca
More informationOnline generation of profile association rules
Online generation of profile association rules Charu C. Aggarwal T.J. Watson Research Center Yorktown Heights, NY 10598 charu@watson.ibm.com Zheng Sun Duke University, Durham, NC-27706 sunz@cs.duke.edu
More informationPROJECT PERIODIC REPORT
PROJECT PERIODIC REPORT Grant Agreement number: 257403 Project acronym: CUBIST Project title: Combining and Uniting Business Intelligence and Semantic Technologies Funding Scheme: STREP Date of latest
More informationMining Association Rules in Temporal Document Collections
Mining Association Rules in Temporal Document Collections Kjetil Nørvåg, Trond Øivind Eriksen, and Kjell-Inge Skogstad Dept. of Computer and Information Science, NTNU 7491 Trondheim, Norway Abstract. In
More informationDiscovering Periodic Patterns in Database Audit Trails
Vol.29 (DTA 2013), pp.365-371 http://dx.doi.org/10.14257/astl.2013.29.76 Discovering Periodic Patterns in Database Audit Trails Marcin Zimniak 1, Janusz R. Getta 2, and Wolfgang Benn 1 1 Faculty of Computer
More informationBINARY DECISION TREE FOR ASSOCIATION RULES MINING IN INCREMENTAL DATABASES
BINARY DECISION TREE FOR ASSOCIATION RULES MINING IN INCREMENTAL DATABASES Amaranatha Reddy P, Pradeep G and Sravani M Department of Computer Science & Engineering, SoET, SPMVV, Tirupati ABSTRACT This
More informationAn Efficient Reduced Pattern Count Tree Method for Discovering Most Accurate Set of Frequent itemsets
IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008 121 An Efficient Reduced Pattern Count Tree Method for Discovering Most Accurate Set of Frequent itemsets
More informationUsing Pattern-Join and Purchase-Combination for Mining Web Transaction Patterns in an Electronic Commerce Environment
Using Pattern-Join and Purchase-Combination for Mining Web Transaction Patterns in an Electronic Commerce Environment Ching-Huang Yun and Ming-Syan Chen Department of Electrical Engineering National Taiwan
More informationVisualisation of ATM network connectivity and topology
Visualisation of ATM network connectivity and topology Oliver Saal and Edwin Blake CS00-13-00 Collaborative Visual Computing Laboratory Department of Computer Science University of Cape Town Private Bag,
More information