CHAPTER 4 K-MEANS AND UCAM CLUSTERING ALGORITHM
|
|
- Lee Terry
- 6 years ago
- Views:
Transcription
1 CHAPTER 4 K-MEANS AND UCAM CLUSTERING 4.1 Introduction ALGORITHM Clustering has been used in a number of applications such as engineering, biology, medicine and data mining. The most popular clustering algorithm used in several field is K-Means since it is very simple, fast and efficient. K-means is developed by Mac Queen. The K-Means algorithm is effective in producing cluster for many practical applications. But the computational complexity of the original K-Means algorithm is very high, especially for large datasets. The K- Means algorithm is a partition clustering method that separates data into K groups. Main drawback of this algorithm is that of a priori fixation of number of clusters and seeds [16]. To rectify the drawbacks of K-means algorithm a new algorithm is proposed namely Unique Clustering with Affinity Measures (UCAM) clustering algorithm which starts its computation without representing the number of clusters and the initial seeds. UCAM clustering algorithm purely works on affinity measure which helps to fix the number of resultant clusters. It divides the dataset into some number of clusters with the help of threshold value.the uniqueness of the cluster is based on the threshold value.the number of clusters increases on decreasing the threshold value and the number of cluster decreases by increasing the
2 threshold value. More unique cluster is obtained when the threshold value is smaller. 4.2 K-Mean Clustering The main objective of cluster is to group the object that are similar in one cluster and separate objects that are dissimilar by assigning them to different clusters. One of the most popular clustering methods is K-Means clusters algorithm. It classifies objects to pre-defined number of clusters, which is given by the user (assume K clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each other. In this algorithm Euclidean distance measure is used between two multidimensional data points X = (x 1,x 2,x 3, x m ) (4.1) Y = (y 1,y 2,y 3, y m ) (4.2) The Euclidean distance measure between the above points x and y are described as follows: D(X, Y) = ( ( x i - y i ) 2 ) 1/2 (4.3) The K-Means method aims to minimize the sum of squared distances between all points and the cluster centre. The algorithmic steps are described in the following Figure 4.1.
3 Input: D = {d 1, d 2, d 3,..., d n } // Set of n data points. K - Number of desired clusters Output: A set of K clusters. Method: 1. Select the number of clusters. Let this number be k. 2. Pick k seeds as centroids of the k clusters. The seeds may be picked randomly unless the user has some insight into the data. 3. Compute the Euclidean distance of each object in the dataset from each of the centroids. 4. Allocate each object to the cluster nearest, based on the distances computed in the previous step. 5. Compute the centroids of the clusters by computing the means of the attribute values if the objects are in each cluster. 6. Check if the stopping criterion has been met (e.g. the cluster membership is unchanged). If yes, go to step 7. If not go to step [Optional] One may decide to stop at this stage or to split a cluster or combine two clusters heuristically until a stopping criterion is met. Figure 4.1: K-Means Clustering Algorithm Though the K-Means algorithm is simple, it has some drawbacks in its quality of the final clustering, since it is highly depends on the initial centroids.
4 Implementing K-Means clustering algorithm in a very small sample data with ten student s information which contains student number, age and marks obtained in three subjects as shown in Table 4.1. Table 4.1: Students Information S S S S S S S S S S The process of K-Mean clustering is initiated with initial seeds which are selected either sequentially or randomly. Each seed acts as centroid for the cluster in the initial stage. In this example three initial seeds are selected in sequential manner. The objects S 1, S 2 and S 3 are the initial seed as represented in the below Table 4.2
5 Table 4.2: The three seeds from Table 4.1 S S S K-Means algorithm produces the following result by applying it on the sample data in Table 4.1. The process is initialized with the seeds as indicated in the 4.2 and produce the results with three clusters which is listed in the following Table 4.3 with 2 objects, Table 4.4 and Table 4.5 is also with 4 objects each. Table 4.3 Cluster C 1 obtained through K-Means S S Table 4.4 Cluster C 2 obtained through K-Means S S S S
6 Table 4.5 Cluster C 3 obtained through K-Means S S S S The K-Means execution results with three clusters as noted below C 1 = { S 1,S 9 } (4.4) C 2 = {S 2, S 5, S 6, S 10 } (4.5) C 3 = {S 3, S 4, S 7, S 8 } (4.6) Where S 1,S 2, S 10 Student s details which considers only numeric attributes. In the above study of K-Means clustering algorithm results with three clusters where low marks and high marks are found in all clusters, since the initial seeds do not have any seeds with the marks above 90. Hence, if the initial seeds or not defined properly then the result won t be unique and more over if it is constrained it will have only three clusters. In K-Means the initial seeds are randomly selected and hence result of two executions on the same data set will not get the same result unless the initial seeds are same. The main drawback in K-Means is
7 that initial seeds and number of cluster should be defined though it is difficult to predict it, in the early stage UCAM Clustering Algorithm In cluster analysis, one does not know what classes or clusters exist and the problem to be solved is to group the given data into meaningful clusters. Here on the same motive UCAM algorithm is developed. clustering algorithm basically for numeric data s. UCAM algorithm is a It mainly focuses on the drawback of K-Means clustering algorithm. In K-Means algorithm, the process is initiated with the initial seeds and number of cluster to be obtained. But the number of cluster that is to be obtained cannot be predicted on a single view of the dataset. The result may not be unique if the number of cluster and the initial seed is not properly identified. UCAM algorithm is implemented with the help of affinity measure for clustering. The process of clustering in UCAM initiated without any centorid and number of clusters that is to be produced. But it sets the threshold value for making unique clusters. By increasing and decreasing the threshold value fixes the number of resultant cluster [85]. The step by step procedure for UCAM is given below in the Figure 4.2 Input: D = {d 1, d 2, d 3... d n } // Set of n data points. T Threshold value. Output: Clusters. Number of cluster depends on affinity measure.
8 Method: 1. Set the threshold value T. 2. Create new cluster structure if it is the first tuple of the dataset. 3. If it is not first tuple compute similarity measure with existing clusters. 4. Get the minimum value of computed similarity, S. 5. Get the cluster index of C i which corresponds to S. 6. If S<=T, then add current tuple to C i. 7. If S>T, create new cluster. 8. Continue the process until the last tuple of the dataset. Figure 4.2 UCAM Clustering Algorithm Implementing UCAM algorithm with the sample data given in Table 4.1. The process is initiated with threshold value T and results with following 5 clusters as shown below is listed in Table 4.6 with 3 objects, Table 4.7 with 3 objects, Table 4.8 with 2 objects, Table 4.9 and Table 4.10 with 1 object. Table 4.6 Cluster C 1 obtained through UCAM S S S
9 Table 4.7 Cluster C 2 obtained through UCAM S S S Table 4.8 Cluster C 3 obtained through UCAM S S Table 4.9 Cluster C 4 obtained through UCAM S
10 Table 4.10 Cluster C 5 obtained through UCAM Stud-no age Mark1 Mark2 Mark3 S The UCAM execution results with five clusters which is noted below C 1 = { S 1,S 3, S 7 } (4.7) C 2 = {S 2, S 5, S 6 } (4.8) C 3 = { S 4, S 8 } (4.9) C 4 = { S 9 } (4.10) C 5 = {S 10 } (4.11) Uniqueness of the cluster depends on the initial setting of the threshold value. If the threshold value increases number of cluster decreases. In UCAM there is no initial prediction on number of resultant cluster. Here, in this algorithm resultant cluster purely based on the affinity measure. In the above study of K-Means clustering algorithm results with three clusters where low marks and high marks are found in all clusters, since the initial seeds do not have any seeds with the marks above 90. Hence if the initial
11 seeds are not defined properly then the result won t be unique and more over if it is constrained it will have only three clusters. In UCAM algorithm is initiated with the threshold alone which produces unique result with five clusters. C 1 Cluster with medium marks. C 2 Cluster with high marks. C 3 Cluster with low marks. C 4 = { S 9 } (4.12) C 5 = {S 10 } (4.13) S 9 is the student with good mark in two subjects and low mark in one subject. So, S 9 should be considered with more care in subject 3 so that it increases ranking of the institution. And S10 should be considered since his age is unique than other students. Both approximate clustering and unique cluster can be obtained by increasing and decreasing the threshold values Measurements on Cluster Uniqueness The cluster representation of K-Mean and UCAM are illustrated through scatter graph as shown in below Figure 4.3 in which each symbol indicates a separate cluster.
12 Figure 4.3 : Clustering through K-Means Figure 4.4 Clustering through UCAM In the above graph Figure 4.4 all the clusters are unique in representation compared to K-Means clustering and the dark shaded symbols are peculiar objects, based on the application it is projected out otherwise it merges with nearby cluster by adjusting the threshold value. Both approximate cluster and unique cluster are obtained by increasing and decreasing the threshold values.
13 4.5 Comparative Analysis UCAM algorithm produces unique clustering only on the bases of affinity measure, hence there is no possibility of error in clustering. One major advantage is that both rough clustering and accurate unique clustering is possible by adjusting the threshold value. But in K-Means clustering there is a chance of getting error if the initial seeds are not identified properly. The comparative study of K-Means and UCAM clustering are shown in the following Table Table 4.11: Comparative study on K-Means and UCAM Clustering Algorithms Initial number of clusters Centriod Threshold value Cluster result Cluster Error K-Means K Initial seeds - Depend on initial seeds Yes, if wrong seeds UCAM - - T Depend on threshold value Discussion Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this chapter the traditional K-Means
14 algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. The K-Means algorithm should be initiated with the number of cluster k and initial seeds. For real time large database it s difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current chapter focused on developing the UCAM(Unique Clustering with Affinity Measure) algorithm for clustering without giving initial seed and number of clusters. Unique clustering is obtained with the help of affinity measures. 4.7 Summary In this chapter, new UCAM algorithm is used for data clustering. This approach reduces the overheads of fixing the cluster size and initial seeds as in K- Means. It fixes threshold value to obtain a unique clustering. The proposed method improves the scalability and reduces the clustering error. This approach ensures that the total mechanism of clustering is in time without loss in correctness of clusters.
CHAPTER 4: CLUSTER ANALYSIS
CHAPTER 4: CLUSTER ANALYSIS WHAT IS CLUSTER ANALYSIS? A cluster is a collection of data-objects similar to one another within the same group & dissimilar to the objects in other groups. Cluster analysis
More informationUnsupervised Learning
Outline Unsupervised Learning Basic concepts K-means algorithm Representation of clusters Hierarchical clustering Distance functions Which clustering algorithm to use? NN Supervised learning vs. unsupervised
More information[7.3, EA], [9.1, CMB]
K-means Clustering Ke Chen Reading: [7.3, EA], [9.1, CMB] Outline Introduction K-means Algorithm Example How K-means partitions? K-means Demo Relevant Issues Application: Cell Neulei Detection Summary
More informationK-Means. Oct Youn-Hee Han
K-Means Oct. 2015 Youn-Hee Han http://link.koreatech.ac.kr ²K-Means algorithm An unsupervised clustering algorithm K stands for number of clusters. It is typically a user input to the algorithm Some criteria
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Cluster Analysis: Basic Concepts and Methods Huan Sun, CSE@The Ohio State University Slides adapted from UIUC CS412, Fall 2017, by Prof. Jiawei Han 2 Chapter 10. Cluster
More informationUnsupervised Learning
Unsupervised Learning Unsupervised learning Until now, we have assumed our training samples are labeled by their category membership. Methods that use labeled samples are said to be supervised. However,
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Cluster Analysis: Basic Concepts and Methods Huan Sun, CSE@The Ohio State University 09/25/2017 Slides adapted from UIUC CS412, Fall 2017, by Prof. Jiawei Han 2 Chapter 10.
More informationINF4820, Algorithms for AI and NLP: Evaluating Classifiers Clustering
INF4820, Algorithms for AI and NLP: Evaluating Classifiers Clustering Erik Velldal University of Oslo Sept. 18, 2012 Topics for today 2 Classification Recap Evaluating classifiers Accuracy, precision,
More informationAPPLICATION OF MULTIPLE RANDOM CENTROID (MRC) BASED K-MEANS CLUSTERING ALGORITHM IN INSURANCE A REVIEW ARTICLE
APPLICATION OF MULTIPLE RANDOM CENTROID (MRC) BASED K-MEANS CLUSTERING ALGORITHM IN INSURANCE A REVIEW ARTICLE Sundari NallamReddy, Samarandra Behera, Sanjeev Karadagi, Dr. Anantha Desik ABSTRACT: Tata
More informationA Review of K-mean Algorithm
A Review of K-mean Algorithm Jyoti Yadav #1, Monika Sharma *2 1 PG Student, CSE Department, M.D.U Rohtak, Haryana, India 2 Assistant Professor, IT Department, M.D.U Rohtak, Haryana, India Abstract Cluster
More informationECLT 5810 Clustering
ECLT 5810 Clustering What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping
More informationINF4820. Clustering. Erik Velldal. Nov. 17, University of Oslo. Erik Velldal INF / 22
INF4820 Clustering Erik Velldal University of Oslo Nov. 17, 2009 Erik Velldal INF4820 1 / 22 Topics for Today More on unsupervised machine learning for data-driven categorization: clustering. The task
More informationData Mining and Data Warehousing Classification-Lazy Learners
Motivation Data Mining and Data Warehousing Classification-Lazy Learners Lazy Learners are the most intuitive type of learners and are used in many practical scenarios. The reason of their popularity is
More informationECLT 5810 Clustering
ECLT 5810 Clustering What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping
More informationLesson 3. Prof. Enza Messina
Lesson 3 Prof. Enza Messina Clustering techniques are generally classified into these classes: PARTITIONING ALGORITHMS Directly divides data points into some prespecified number of clusters without a hierarchical
More informationUnsupervised Data Mining: Clustering. Izabela Moise, Evangelos Pournaras, Dirk Helbing
Unsupervised Data Mining: Clustering Izabela Moise, Evangelos Pournaras, Dirk Helbing Izabela Moise, Evangelos Pournaras, Dirk Helbing 1 1. Supervised Data Mining Classification Regression Outlier detection
More informationCLUSTERING. CSE 634 Data Mining Prof. Anita Wasilewska TEAM 16
CLUSTERING CSE 634 Data Mining Prof. Anita Wasilewska TEAM 16 1. K-medoids: REFERENCES https://www.coursera.org/learn/cluster-analysis/lecture/nj0sb/3-4-the-k-medoids-clustering-method https://anuradhasrinivas.files.wordpress.com/2013/04/lesson8-clustering.pdf
More informationGene Clustering & Classification
BINF, Introduction to Computational Biology Gene Clustering & Classification Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Introduction to Gene Clustering
More informationDocument Clustering: Comparison of Similarity Measures
Document Clustering: Comparison of Similarity Measures Shouvik Sachdeva Bhupendra Kastore Indian Institute of Technology, Kanpur CS365 Project, 2014 Outline 1 Introduction The Problem and the Motivation
More information11/2/2017 MIST.6060 Business Intelligence and Data Mining 1. Clustering. Two widely used distance metrics to measure the distance between two records
11/2/2017 MIST.6060 Business Intelligence and Data Mining 1 An Example Clustering X 2 X 1 Objective of Clustering The objective of clustering is to group the data into clusters such that the records within
More informationIntelligent Image and Graphics Processing
Intelligent Image and Graphics Processing 智能图像图形处理图形处理 布树辉 bushuhui@nwpu.edu.cn http://www.adv-ci.com Clustering Clustering Attach label to each observation or data points in a set You can say this unsupervised
More informationBBS654 Data Mining. Pinar Duygulu. Slides are adapted from Nazli Ikizler
BBS654 Data Mining Pinar Duygulu Slides are adapted from Nazli Ikizler 1 Classification Classification systems: Supervised learning Make a rational prediction given evidence There are several methods for
More informationClustering. K-means clustering
Clustering K-means clustering Clustering Motivation: Identify clusters of data points in a multidimensional space, i.e. partition the data set {x 1,...,x N } into K clusters. Intuition: A cluster is a
More informationCMPUT 391 Database Management Systems. Data Mining. Textbook: Chapter (without 17.10)
CMPUT 391 Database Management Systems Data Mining Textbook: Chapter 17.7-17.11 (without 17.10) University of Alberta 1 Overview Motivation KDD and Data Mining Association Rules Clustering Classification
More informationExploratory Analysis: Clustering
Exploratory Analysis: Clustering (some material taken or adapted from slides by Hinrich Schutze) Heejun Kim June 26, 2018 Clustering objective Grouping documents or instances into subsets or clusters Documents
More informationAssociation Rule Mining and Clustering
Association Rule Mining and Clustering Lecture Outline: Classification vs. Association Rule Mining vs. Clustering Association Rule Mining Clustering Types of Clusters Clustering Algorithms Hierarchical:
More informationCluster Analysis. Ying Shen, SSE, Tongji University
Cluster Analysis Ying Shen, SSE, Tongji University Cluster analysis Cluster analysis groups data objects based only on the attributes in the data. The main objective is that The objects within a group
More informationClustering and Visualisation of Data
Clustering and Visualisation of Data Hiroshi Shimodaira January-March 28 Cluster analysis aims to partition a data set into meaningful or useful groups, based on distances between data points. In some
More informationOlmo S. Zavala Romero. Clustering Hierarchical Distance Group Dist. K-means. Center of Atmospheric Sciences, UNAM.
Center of Atmospheric Sciences, UNAM November 16, 2016 Cluster Analisis Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster)
More informationData Mining. Dr. Raed Ibraheem Hamed. University of Human Development, College of Science and Technology Department of Computer Science
Data Mining Dr. Raed Ibraheem Hamed University of Human Development, College of Science and Technology Department of Computer Science 2016 201 Road map What is Cluster Analysis? Characteristics of Clustering
More informationEnhancing K-means Clustering Algorithm with Improved Initial Center
Enhancing K-means Clustering Algorithm with Improved Initial Center Madhu Yedla #1, Srinivasa Rao Pathakota #2, T M Srinivasa #3 # Department of Computer Science and Engineering, National Institute of
More informationData clustering & the k-means algorithm
April 27, 2016 Why clustering? Unsupervised Learning Underlying structure gain insight into data generate hypotheses detect anomalies identify features Natural classification e.g. biological organisms
More informationUniversity of Florida CISE department Gator Engineering. Clustering Part 2
Clustering Part 2 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville Partitional Clustering Original Points A Partitional Clustering Hierarchical
More informationComparative Study Of Different Data Mining Techniques : A Review
Volume II, Issue IV, APRIL 13 IJLTEMAS ISSN 7-5 Comparative Study Of Different Data Mining Techniques : A Review Sudhir Singh Deptt of Computer Science & Applications M.D. University Rohtak, Haryana sudhirsingh@yahoo.com
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Erik Velldal & Stephan Oepen Language Technology Group (LTG) September 23, 2015 Agenda Last week Supervised vs unsupervised learning.
More informationNew Approach for K-mean and K-medoids Algorithm
New Approach for K-mean and K-medoids Algorithm Abhishek Patel Department of Information & Technology, Parul Institute of Engineering & Technology, Vadodara, Gujarat, India Purnima Singh Department of
More informationClustering. Supervised vs. Unsupervised Learning
Clustering Supervised vs. Unsupervised Learning So far we have assumed that the training samples used to design the classifier were labeled by their class membership (supervised learning) We assume now
More informationNORMALIZATION INDEXING BASED ENHANCED GROUPING K-MEAN ALGORITHM
NORMALIZATION INDEXING BASED ENHANCED GROUPING K-MEAN ALGORITHM Saroj 1, Ms. Kavita2 1 Student of Masters of Technology, 2 Assistant Professor Department of Computer Science and Engineering JCDM college
More informationCOSC 6339 Big Data Analytics. Fuzzy Clustering. Some slides based on a lecture by Prof. Shishir Shah. Edgar Gabriel Spring 2017.
COSC 6339 Big Data Analytics Fuzzy Clustering Some slides based on a lecture by Prof. Shishir Shah Edgar Gabriel Spring 217 Clustering Clustering is a technique for finding similarity groups in data, called
More informationCLUSTERING BIG DATA USING NORMALIZATION BASED k-means ALGORITHM
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IMPACT FACTOR: 5.258 IJCSMC,
More informationA Comparative study of Clustering Algorithms using MapReduce in Hadoop
A Comparative study of Clustering Algorithms using MapReduce in Hadoop Dweepna Garg 1, Khushboo Trivedi 2, B.B.Panchal 3 1 Department of Computer Science and Engineering, Parul Institute of Engineering
More informationRoad map. Basic concepts
Clustering Basic concepts Road map K-means algorithm Representation of clusters Hierarchical clustering Distance functions Data standardization Handling mixed attributes Which clustering algorithm to use?
More informationUnsupervised Learning and Clustering
Unsupervised Learning and Clustering Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2009 CS 551, Spring 2009 c 2009, Selim Aksoy (Bilkent University)
More informationUnsupervised Learning and Clustering
Unsupervised Learning and Clustering Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2008 CS 551, Spring 2008 c 2008, Selim Aksoy (Bilkent University)
More informationCOSC 6397 Big Data Analytics. Fuzzy Clustering. Some slides based on a lecture by Prof. Shishir Shah. Edgar Gabriel Spring 2015.
COSC 6397 Big Data Analytics Fuzzy Clustering Some slides based on a lecture by Prof. Shishir Shah Edgar Gabriel Spring 215 Clustering Clustering is a technique for finding similarity groups in data, called
More informationData Mining. 3.2 Decision Tree Classifier. Fall Instructor: Dr. Masoud Yaghini. Chapter 5: Decision Tree Classifier
Data Mining 3.2 Decision Tree Classifier Fall 2008 Instructor: Dr. Masoud Yaghini Outline Introduction Basic Algorithm for Decision Tree Induction Attribute Selection Measures Information Gain Gain Ratio
More informationJarek Szlichta
Jarek Szlichta http://data.science.uoit.ca/ Approximate terminology, though there is some overlap: Data(base) operations Executing specific operations or queries over data Data mining Looking for patterns
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 4th, 2014 Wolf-Tilo Balke and José Pinto Institut für Informationssysteme Technische Universität Braunschweig The Cluster
More informationMachine Learning (BSMC-GA 4439) Wenke Liu
Machine Learning (BSMC-GA 4439) Wenke Liu 01-31-017 Outline Background Defining proximity Clustering methods Determining number of clusters Comparing two solutions Cluster analysis as unsupervised Learning
More informationData Mining. 3.5 Lazy Learners (Instance-Based Learners) Fall Instructor: Dr. Masoud Yaghini. Lazy Learners
Data Mining 3.5 (Instance-Based Learners) Fall 2008 Instructor: Dr. Masoud Yaghini Outline Introduction k-nearest-neighbor Classifiers References Introduction Introduction Lazy vs. eager learning Eager
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Murhaf Fares & Stephan Oepen Language Technology Group (LTG) September 27, 2017 Today 2 Recap Evaluation of classifiers Unsupervised
More informationA REVIEW ON K-mean ALGORITHM AND IT S DIFFERENT DISTANCE MATRICS
A REVIEW ON K-mean ALGORITHM AND IT S DIFFERENT DISTANCE MATRICS Rashmi Sindhu 1, Rainu Nandal 2, Priyanka Dhamija 3, Harkesh Sehrawat 4, Kamaldeep Computer Science and Engineering, University Institute
More informationClustering: Overview and K-means algorithm
Clustering: Overview and K-means algorithm Informal goal Given set of objects and measure of similarity between them, group similar objects together K-Means illustrations thanks to 2006 student Martin
More informationAccelerating Unique Strategy for Centroid Priming in K-Means Clustering
IJIRST International Journal for Innovative Research in Science & Technology Volume 3 Issue 07 December 2016 ISSN (online): 2349-6010 Accelerating Unique Strategy for Centroid Priming in K-Means Clustering
More informationCS 2750 Machine Learning. Lecture 19. Clustering. CS 2750 Machine Learning. Clustering. Groups together similar instances in the data sample
Lecture 9 Clustering Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square Clustering Groups together similar instances in the data sample Basic clustering problem: distribute data into k different groups
More informationData Informatics. Seon Ho Kim, Ph.D.
Data Informatics Seon Ho Kim, Ph.D. seonkim@usc.edu Clustering Overview Supervised vs. Unsupervised Learning Supervised learning (classification) Supervision: The training data (observations, measurements,
More informationUnsupervised Learning Partitioning Methods
Unsupervised Learning Partitioning Methods Road Map 1. Basic Concepts 2. K-Means 3. K-Medoids 4. CLARA & CLARANS Cluster Analysis Unsupervised learning (i.e., Class label is unknown) Group data to form
More informationHard clustering. Each object is assigned to one and only one cluster. Hierarchical clustering is usually hard. Soft (fuzzy) clustering
An unsupervised machine learning problem Grouping a set of objects in such a way that objects in the same group (a cluster) are more similar (in some sense or another) to each other than to those in other
More informationBasic Data Mining Technique
Basic Data Mining Technique What is classification? What is prediction? Supervised and Unsupervised Learning Decision trees Association rule K-nearest neighbor classifier Case-based reasoning Genetic algorithm
More informationComparision between Quad tree based K-Means and EM Algorithm for Fault Prediction
Comparision between Quad tree based K-Means and EM Algorithm for Fault Prediction Swapna M. Patil Dept.Of Computer science and Engineering,Walchand Institute Of Technology,Solapur,413006 R.V.Argiddi Assistant
More informationArtificial Intelligence. Programming Styles
Artificial Intelligence Intro to Machine Learning Programming Styles Standard CS: Explicitly program computer to do something Early AI: Derive a problem description (state) and use general algorithms to
More information6. Learning Partitions of a Set
6. Learning Partitions of a Set Also known as clustering! Usually, we partition sets into subsets with elements that are somewhat similar (and since similarity is often task dependent, different partitions
More informationUnsupervised Learning : Clustering
Unsupervised Learning : Clustering Things to be Addressed Traditional Learning Models. Cluster Analysis K-means Clustering Algorithm Drawbacks of traditional clustering algorithms. Clustering as a complex
More informationINITIALIZING CENTROIDS FOR K-MEANS ALGORITHM AN ALTERNATIVE APPROACH
Volume 118 No. 18 2018, 1565-1570 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu INITIALIZING CENTROIDS FOR K-MEANS ALGORITHM AN ALTERNATIVE APPROACH
More informationCHAPTER 3 A FAST K-MODES CLUSTERING ALGORITHM TO WAREHOUSE VERY LARGE HETEROGENEOUS MEDICAL DATABASES
70 CHAPTER 3 A FAST K-MODES CLUSTERING ALGORITHM TO WAREHOUSE VERY LARGE HETEROGENEOUS MEDICAL DATABASES 3.1 INTRODUCTION In medical science, effective tools are essential to categorize and systematically
More informationClustering CS 550: Machine Learning
Clustering CS 550: Machine Learning This slide set mainly uses the slides given in the following links: http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap8_basic_cluster_analysis.pdf
More informationAnalyzing Outlier Detection Techniques with Hybrid Method
Analyzing Outlier Detection Techniques with Hybrid Method Shruti Aggarwal Assistant Professor Department of Computer Science and Engineering Sri Guru Granth Sahib World University. (SGGSWU) Fatehgarh Sahib,
More informationMultivariate Analysis
Multivariate Analysis Cluster Analysis Prof. Dr. Anselmo E de Oliveira anselmo.quimica.ufg.br anselmo.disciplinas@gmail.com Unsupervised Learning Cluster Analysis Natural grouping Patterns in the data
More informationLecture-17: Clustering with K-Means (Contd: DT + Random Forest)
Lecture-17: Clustering with K-Means (Contd: DT + Random Forest) Medha Vidyotma April 24, 2018 1 Contd. Random Forest For Example, if there are 50 scholars who take the measurement of the length of the
More informationIntroduction to Computer Science
DM534 Introduction to Computer Science Clustering and Feature Spaces Richard Roettger: About Me Computer Science (Technical University of Munich and thesis at the ICSI at the University of California at
More informationClustering. Informal goal. General types of clustering. Applications: Clustering in information search and analysis. Example applications in search
Informal goal Clustering Given set of objects and measure of similarity between them, group similar objects together What mean by similar? What is good grouping? Computation time / quality tradeoff 1 2
More informationCHAPTER 3 ASSOCIATON RULE BASED CLUSTERING
41 CHAPTER 3 ASSOCIATON RULE BASED CLUSTERING 3.1 INTRODUCTION This chapter describes the clustering process based on association rule mining. As discussed in the introduction, clustering algorithms have
More informationAdministrative. Machine learning code. Supervised learning (e.g. classification) Machine learning: Unsupervised learning" BANANAS APPLES
Administrative Machine learning: Unsupervised learning" Assignment 5 out soon David Kauchak cs311 Spring 2013 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture17-clustering.ppt Machine
More informationWhat is Clustering? Clustering. Characterizing Cluster Methods. Clusters. Cluster Validity. Basic Clustering Methodology
Clustering Unsupervised learning Generating classes Distance/similarity measures Agglomerative methods Divisive methods Data Clustering 1 What is Clustering? Form o unsupervised learning - no inormation
More informationClustering: Overview and K-means algorithm
Clustering: Overview and K-means algorithm Informal goal Given set of objects and measure of similarity between them, group similar objects together K-Means illustrations thanks to 2006 student Martin
More informationCHAPTER-6 WEB USAGE MINING USING CLUSTERING
CHAPTER-6 WEB USAGE MINING USING CLUSTERING 6.1 Related work in Clustering Technique 6.2 Quantifiable Analysis of Distance Measurement Techniques 6.3 Approaches to Formation of Clusters 6.4 Conclusion
More informationAutomatic Cluster Number Selection using a Split and Merge K-Means Approach
Automatic Cluster Number Selection using a Split and Merge K-Means Approach Markus Muhr and Michael Granitzer 31st August 2009 The Know-Center is partner of Austria's Competence Center Program COMET. Agenda
More informationImproved Performance of Unsupervised Method by Renovated K-Means
Improved Performance of Unsupervised Method by Renovated P.Ashok Research Scholar, Bharathiar University, Coimbatore Tamilnadu, India. ashokcutee@gmail.com Dr.G.M Kadhar Nawaz Department of Computer Application
More informationWorking with Unlabeled Data Clustering Analysis. Hsiao-Lung Chan Dept Electrical Engineering Chang Gung University, Taiwan
Working with Unlabeled Data Clustering Analysis Hsiao-Lung Chan Dept Electrical Engineering Chang Gung University, Taiwan chanhl@mail.cgu.edu.tw Unsupervised learning Finding centers of similarity using
More informationMIS2502: Data Analytics Clustering and Segmentation. Jing Gong
MIS2502: Data Analytics Clustering and Segmentation Jing Gong gong@temple.edu http://community.mis.temple.edu/gong What is Cluster Analysis? Grouping data so that elements in a group will be Similar (or
More informationData Mining: Classifier Evaluation. CSCI-B490 Seminar in Computer Science (Data Mining)
Data Mining: Classifier Evaluation CSCI-B490 Seminar in Computer Science (Data Mining) Predictor Evaluation 1. Question: how good is our algorithm? how will we estimate its performance? 2. Question: what
More informationIntroduction to Data Mining
Introduction to Data Mining Lecture #14: Clustering Seoul National University 1 In This Lecture Learn the motivation, applications, and goal of clustering Understand the basic methods of clustering (bottom-up
More informationIteration Reduction K Means Clustering Algorithm
Iteration Reduction K Means Clustering Algorithm Kedar Sawant 1 and Snehal Bhogan 2 1 Department of Computer Engineering, Agnel Institute of Technology and Design, Assagao, Goa 403507, India 2 Department
More informationClustering in Data Mining
Clustering in Data Mining Classification Vs Clustering When the distribution is based on a single parameter and that parameter is known for each object, it is called classification. E.g. Children, young,
More informationUnsupervised Learning. Presenter: Anil Sharma, PhD Scholar, IIIT-Delhi
Unsupervised Learning Presenter: Anil Sharma, PhD Scholar, IIIT-Delhi Content Motivation Introduction Applications Types of clustering Clustering criterion functions Distance functions Normalization Which
More informationSupervised vs. Unsupervised Learning
Clustering Supervised vs. Unsupervised Learning So far we have assumed that the training samples used to design the classifier were labeled by their class membership (supervised learning) We assume now
More informationClustering Algorithms In Data Mining
2017 5th International Conference on Computer, Automation and Power Electronics (CAPE 2017) Clustering Algorithms In Data Mining Xiaosong Chen 1, a 1 Deparment of Computer Science, University of Vermont,
More informationClustering. RNA-seq: What is it good for? Finding Similarly Expressed Genes. Data... And Lots of It!
RNA-seq: What is it good for? Clustering High-throughput RNA sequencing experiments (RNA-seq) offer the ability to measure simultaneously the expression level of thousands of genes in a single experiment!
More informationClustering: Centroid-Based Partitioning
Clustering: Centroid-Based Partitioning Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong 1 / 29 Y Tao Clustering: Centroid-Based Partitioning In this lecture, we
More informationRedefining and Enhancing K-means Algorithm
Redefining and Enhancing K-means Algorithm Nimrat Kaur Sidhu 1, Rajneet kaur 2 Research Scholar, Department of Computer Science Engineering, SGGSWU, Fatehgarh Sahib, Punjab, India 1 Assistant Professor,
More informationECG782: Multidimensional Digital Signal Processing
ECG782: Multidimensional Digital Signal Processing Object Recognition http://www.ee.unlv.edu/~b1morris/ecg782/ 2 Outline Knowledge Representation Statistical Pattern Recognition Neural Networks Boosting
More informationGlobal Journal of Engineering Science and Research Management
ADVANCED K-MEANS ALGORITHM FOR BRAIN TUMOR DETECTION USING NAIVE BAYES CLASSIFIER Veena Bai K*, Dr. Niharika Kumar * MTech CSE, Department of Computer Science and Engineering, B.N.M. Institute of Technology,
More informationI211: Information infrastructure II
Data Mining: Classifier Evaluation I211: Information infrastructure II 3-nearest neighbor labeled data find class labels for the 4 data points 1 0 0 6 0 0 0 5 17 1.7 1 1 4 1 7.1 1 1 1 0.4 1 2 1 3.0 0 0.1
More informationk-means, k-means++ Barna Saha March 8, 2016
k-means, k-means++ Barna Saha March 8, 2016 K-Means: The Most Popular Clustering Algorithm k-means clustering problem is one of the oldest and most important problem. K-Means: The Most Popular Clustering
More informationCS 1675 Introduction to Machine Learning Lecture 18. Clustering. Clustering. Groups together similar instances in the data sample
CS 1675 Introduction to Machine Learning Lecture 18 Clustering Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square Clustering Groups together similar instances in the data sample Basic clustering problem:
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering May 25, 2011 Wolf-Tilo Balke and Joachim Selke Institut für Informationssysteme Technische Universität Braunschweig Homework
More informationCluster Analysis. CSE634 Data Mining
Cluster Analysis CSE634 Data Mining Agenda Introduction Clustering Requirements Data Representation Partitioning Methods K-Means Clustering K-Medoids Clustering Constrained K-Means clustering Introduction
More information2. Background. 2.1 Clustering
2. Background 2.1 Clustering Clustering involves the unsupervised classification of data items into different groups or clusters. Unsupervised classificaiton is basically a learning task in which learning
More informationINF 4300 Classification III Anne Solberg The agenda today:
INF 4300 Classification III Anne Solberg 28.10.15 The agenda today: More on estimating classifier accuracy Curse of dimensionality and simple feature selection knn-classification K-means clustering 28.10.15
More informationClustering in Ratemaking: Applications in Territories Clustering
Clustering in Ratemaking: Applications in Territories Clustering Ji Yao, PhD FIA ASTIN 13th-16th July 2008 INTRODUCTION Structure of talk Quickly introduce clustering and its application in insurance ratemaking
More information