Reliable Data Mining Tasks and Techniques for Industrial Applications

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1 Reliable Data Mining Tasks and Techniques for Industrial Applications G.Sabarmathi 1, Dr.R.Chinnaiyan 2 1 Department of Computer Science, Christ Academy Institute for Advance Studies, Bangalore University, Bangalore Department of MCA, New Horizon College of Engineering (Autonomous), VTU, Bangalore 1 sabarganesh@gmail.com, 2 vijayachinns@gmail.com Abstract Data mining is the process of extracting the useful data, patterns and trends from a large amount of data by using techniques like clustering, classification, association and regression. In this paper we are going to discuss about various Data mining applications and techniques used recently. Keywords Data Mining, Patterns, Clustering, Classification, Association 1. INTRODUCTION Data mining is the procedure of extracting knowledge from available data. The result extracted from those data can be used in various applications like Market analysis, Data analysis in various industries like information technology, health care and various other areas. There are two categories of functions involved in data mining they are Descriptive model and Predictive model. Whereas Predictive model helps to analyse the data which is available assuming the unknown or unavailable data in future, in case of Descriptive model it analyse the data with patterns and comes with new and significant information. They follow different pattern in analysing the data. Data mining is the process of finding the information from unknown facts and data which helps in decision making. Figure 0.1: Data Mining: Multiple Disciplines 2. KNOWLEDGE DISCOVERY in DATABASES(KDD) Knowledge Discovery in Database (KDD) is the method of extracting useful information from subordinate databases. KDD involves the steps like data processed from data selection, interpretation, cleansing, transforming the raw data into some information, integrating and evaluating the pattern for that information received. 138

2 Figure 1.1 Process of Knowledge Discovery in Database Above steps shows the process of KDD Data come from various resources are combined as single entity data called target data. Data from target data are pre-processed and transformed into processed data. The result evolved is been transformed to form some patterns by applying some data mining algorithms. Finally those results are interpreted to form some useful information called knowledge. 3. DATA MINING TEHNIQUES Often used techniques in data mining are: Data mining software uses advanced pattern recognition algorithms to shift through large amounts of data. This assists discovery of unknown business information at strategic level.[5] Sequence mining is a technique used in Human Genetics to learn and understand the relationship between the interindividual variations in human DNA sequence and the variability in disease susceptibility.[5] Spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. Information Retrieval[6] 4. DATA MINING TASKS Classification is the most commonly applied data mining technique. But it requires set of pre-classified examples to create a model. Based on the model the classification shall be applied on large dataset. Credit risk and fraud detection is identified using classification.[9] Support vector machines are supervised learning models with associated learning algorithms that analyse data used for regression analysis and classification. A Support Vector Machine (SVM) performs classification by finding the hyper plane which maximizes the margin between the two classes. The vectors (cases) that define the hyper plane are the support vectors.[9] Decision tree is a widely used data mining method. In decision model, a decision tree is a graph of decisions and their possible significances, represented in the form of branches and nodes. A decision tree contains a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the end result of a test, and each leaf node holds a class label. The topmost node in the tree is the root node. Decision trees use recursive data partitioning to extract useful data.[9] Regressions a task used to predict the number such as Age, height, Income distance etc., It predicts the data where the target data is unrevealed. It is done by the statistical team by measuring the difference between the predicted and expected values. [20] 139

3 Prediction is a method used in predicting the outcome based on available data by incorporating with unavailable data sets of future.it is mainly used in Market Analysis, Medical diagnosis, Fraud detection etc., Time series Analysis is a sequence of event based on the earlier events the next event is analysed. It replicates the process being measured and there are certain components that affect the performance of a process. This includes methods to analyse time-series data in order to extract useful patterns, trends, rules and statistics. Stock market prediction is an important application of time- series analysis.[19] Figure 3.1 categories of Data mining Techniques Association rule analysis is a descriptive data mining function, which involves discovering patterns or associations between the data. It is a method for finding the interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Associative classification evolved by combining associative rule and classification methodology. This helps to identify interesting correlations, frequent patterns, associations or casual structures.[9] Clustering is a process of partitioning a data set into clusters which contains set of meaningful sub-classes. It is a descriptive data mining functions which assembles similar objects in the same cluster and dissimilar objects in different clusters. It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in pattern, machine learning, image analysis, information retrieval, and bioinformatics. Summarization is a simplification of data where a set of data is been summarized that results the combined information of the data. This method helps us to identify the customer behaviour in sales and purchase of the product. 5. RELIABILITY IN DATA MINING A reliability model for data mining systems represents a clear picture of the data functional interdependencies providing a means to trade-off in data mining design alternatives and to identify areas for data mining design improvement of databases. The reliability models for data mining are also helpful in: i. Identifying of critical items and single points of failure of data ii. Allocating reliability goals to portions of the design of database and mining iii. Providing a framework for comparing estimated reliability of data mining methods Trading-off alternative fault tolerance approaches for data mining approaches. Reliability of systems is presented by Dr.R.Chinnaiyan and Dr.S.Somasundaram [25, 26, 27, 28, 29, 30, 31, and 32]. The authors evaluated the reliability of systems with novel methods and ensured that the reliability models provides optimized reliability results. The same can be applied to all the data mining tasks and techniques for providing reliable and optimized results. 140

4 6. APPLICATIONS IN DATA MINING Data Mining For Financial Data Analysis In Banking Industry data mining is used for Fraud detection in credit card, Predicting the profit, Analysing the current market trends etc., In financial markets and neural networks data mining is used for Analysing the stock market, Predicting the financial disasters, commodity price etc., In Telecommunications Industry it is used to predict the customer satisfaction, new investment strategy planning, current market study etc. Retail Industry is a main application where it is used to analyse various customer behaviours in buying the product and introducing new product. In medical science it is used in diagnosis of diseases, patient satisfaction, history generation, etc. Mammography Computer Aided Design tools are used in detecting the tumour s. It is used in the sports industry also by scheduling the games for each and every day in the national and international games throughout the world with a huge number of data s. 7. CONCLUSIONS Data mining plays a significant role in finding the different patterns and helps in discovering new ideas from a large datasets. In this we try to group all the different Data Mining techniques based on their category of functions. With the help of these techniques it is helping the business to take decisions on available data sets. It is also been said about the various application areas. In future this paper aims to combine different algorithms used in different techniques and various applications that is involved in those categories. Thus Data Mining is said to be a promising interdisciplinary area in the field of research. REFERENCES [1]. PhridviRaj MSB., GuruRao CV (2013) Data mining past, present and future a typical survey on data streams. INTER-ENG Procedia Technology 12: [2]. Nikhil Bhongade.,ShrikantAkarte.,GauravSawaleA Review On:Data Mining Tools and Application.IJSTMR.,Volume 2, Issue 5, May 2017 [3]. Demšar J, Zupan B (2013) Orange: Data Mining Fruitful and Fun - A Historical Perspective. Informatica 37:55 60 [4]. SangeetaGoele, NishaChanana, Data Mining Trend In Past, Current And Future, International Journal of Computing & Business Research, in Proc. I-Society 2012, [5]. [6]. Spatial Data Analysis: Models, Methods and Techniques,by Jinfeng Wang and Manfred M. Fischer,5 August 2011 [7]. Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic SmythFrom Data Mining to knowledge discovery in databases [8]. DhruvMadanGopal, Aditya R, C Vishnu Kumar Reddy, Gautham S, Nagarathna N, A Survey on Data Mining Applications, Techniques and Challenges in Healthcare, International Journal of Emerging Technologies and Innovative Research, April 2015, Volume 2, Issue 4 [9]. P.Haripriya, R. Porkodi,A Survey Paper on Data mining Techniques and Challenges in Distributed DICOM, IJARCCE, Vol. 5, Issue 3, March 2016 [10]. Indexing Techniques for Data Warehouses Queries,SirirutVanichayobon Le Gruenwald.,The University of Oklahoma School of Computer Science Norman, OK, [11]. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. InkeriVerkamo. Fast discovery of association rules. In U. Fayyad and et al, editors, Advances in Knowledge Discovery and Data Mining, pages AAAI Press, Menlo Park, CA, [12]. Indexing and Data Access Methods for Database Mining,Ganesh Ramesh, William A. Maniatty,Dept. of Computer Science,University at Albany,Albany, NY 12222,Mohammed J. Zaki,Dept. of Computer Science,Rensselaer Polytechnic Institute,Troy, NY

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