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The document compares and summarizes several clustering algorithms, including K-Means, DBSCAN, hierarchical clustering, and CURE. It discusses the time and space complexity of each algorithm, how they are affected by the use of KD trees, their benefits and limitations, and provides examples of their performance on benchmark datasets.
Data miningpresentation
- 1. Comparing Clustering Algorithms Partitioning Algorithms − K-Means − DBSCAN Using KD Trees Hierarchical Algorithms − Agglomerative Clustering − CURE
- 2. K-Means Partitional clustering Prototype based Clustering O(I * K * m * n) Space Complexity Using KD Trees the overall Time Complexity reduces to O(m * logm) Select K initial centroids Repeat − For each point, find its closes centroid and assign that point to the centroid. This results in the formation of K clusters − Recompute centroid for each cluster until the centroids do not change
- 3. K-Means (Contd.) Datasets - SPAETH2 2D dataset of 3360 points
- 9. K-Means (Contd.) Performance Measurements Compiler Used − LabVIEW 8.2.1 Hardware Used − Intel® Core(TM)2 IV 1.73 Ghz − 1 GB RAM Current Status − Done Time Taken − 355 ms / 3360 points
- 10. K-Means (Contd.) Pros Simple Fast for low dimensional data It can find pure sub clusters if large number of clusters is specified Cons K-Means cannot handle non-globular data of different sizes and densities K-Means will not identify outliers K-Means is restricted to data which has the notion of a center (centroid)
- 11. Agglomerative Hierarchical Clustering Starting with one point (singleton) clusters and recursively merging two or more most similar clusters to one "parent" cluster until the termination criterion is reached Algorithms: − MIN (Single Link) − MAX (Complete Link) − Group Average (GA) MIN: susceptible to noise/outliers MAX/GA: may not work well with non- globular clusters CURE tries to handle both problems
- 12. Data Set 2-D data set used − The SPAETH2 dataset is a related collection of data for cluster analysis. (Around 1500 data points)
- 13. Algorithm optimization It involved the implementation of Minimum Spanning Tree using Kruskal’s algorithm Union By Rank method is used to speed-up the algorithm Environment: − Implemented using MATLAB Other Tools: − Gnuplot Present Status − Single Link and Complete Link– Done − Group Average – in progress
- 14. Single Link/CURE Globular Clusters
- 16. Final Cluster
- 17. Single Link / CURE Non globular
- 18. KD Trees K Dimensional Trees Space Partitioning Data Structure Splitting planes perpendicular to Coordinate Axes Useful in Nearest Neighbor Search Reduces the Overall Time Complexity to O(log n) Has been used in many clustering algorithms and other domains
- 19. Clustering Algorithms use KD Trees extensively for improving their Time Complexity Requirements Eg. Fast K-Means, Fast DBSCAN etc We considered 2 popular Clustering Algorithms which use KD Tree Approach to speed up clustering and minimize search time. We used Open Source Implementation of KD Trees (available under GNU GPL)
- 20. DBSCAN (Using KD Trees) Density based Clustering (Maximal Set of Density Connected Points) O(m) Space Complexity Using KD Trees the overall Time Complexity reduces to O(m * logm) from O(m^2) Pros Fast for low dimensional data Can discover clusters of arbitrary shapes Robust towards Outlier Detection (Noise)
- 21. DBSCAN - Issues DBSCAN is very sensitive to clustering parameters MinPoints (Min Neighborhood Points) and EPS (Images Next) The Algorithm is not partitionable for multi- processor systems. DBSCAN fails to identify clusters if density varies and if the data set is too sparse. (Images Next) Sampling Affects Density Measures
- 22. DBSCAN (Contd.) Performance Measurements Compiler Used - Java 1.6 Hardware Used Intel Pentium IV 1.8 Ghz (Duo Core) 1 GB RAM No. of Points 1572 3568 7502 10256 Clustering Time (sec) 3.5 10.9 39.5 78.4 DBSCAN Using KD Trees Performance Measures 120 100 80 60 DBSCAN Using KDTree 40 Basic DBSCAN 20 0 1572 3568 7502 10256
- 23. CURE – Hierarchical Clustering Involves Two Pass clustering Uses Efficient Sampling Algorithms Scalable for Large Datasets First pass of Algorithm is partitionable so that it can run concurrently on multiple processors (Higher number of partitions help keeping execution time linear as size of dataset increase)
- 24. Source - CURE: An Efficient Clustering Algorithm for Large Databases. S. Guha, R. Rastogi and K. Shim, 1998. Each STEP is Important in Achieving Scalability and Efficiency as well as Improving concurrency. Data Structures KD-Tree to store the data/representative points : O(log n) searching time for nearest neighbors Min Heap to Store the Clusters : O(1) searching time to compute next cluster to be processed Cure hence has a O(n) Space Complexity
- 25. CURE (Contd.) Outperforms Basic Hierarchical Clustering by reducing the Time Complexity to O(n^2) from O(n^2*logn) Two Steps of Outlier Elimination − After Pre-clustering − Assigning label to data which was not part of Sample Captures the shape of clusters by selecting the notion of representative points (well scattered points which determine the boundary of cluster)
- 26. CURE - Benefits against Popular Algorithms K-Means (& Centroid based Algorithms) : Unsuitable for non-spherical and size differing clusters. CLARANS : Needs multiple data scan (R* Trees were proposed later on). CURE uses KD Trees inherently to store the dataset and use it across passes. BIRCH : Suffers from identifying only convex or spherical clusters of uniform size DBSCAN : No parallelism, High Sensitivity, Sampling of data may affect density measures.
- 27. CURE (Contd.) Observations towards Sensitivity to Parameters − Random Sample Size : It should be ensured that the sample represents all existing cluster. Algorithm uses Chernoff Bounds to calculate the size − Shrink Factor of Representative Points − Representative Points Computation Time − Number of Partitions : Very high number of partitions (>50) would not give suitable results as some partitions may not have sufficient points to cluster.
- 28. CURE - Performance Compiler : Java 1.6 Hardware Used : Intel Pentium IV 1.8 Ghz (Duo Core) 1 GB RAM No. of Points 1572 3568 7502 10256 Clustering Time (sec) Partition P = 2 6.4 7.8 29.4 75.7 Partition P = 3 6.5 7.6 21.6 43.6 Partition P = 5 6.1 7.3 12.2 21.2 CURE Performance Measurements 90 80 70 P=2 60 P=3 50 P=5 40 DBSCAN 30 20 10 0 1572 3568 7502 10256
- 29. Data Sets and Results SPAETH - http://people.scs.fsu.edu/~burkardt/f_src/spaeth/spaeth.html Synthetic Data - http://dbkgroup.org/handl/generators/
- 30. References An Efficient k-Means Clustering Algorithm: Analysis and Implementation - Tapas Kanungo, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, Angela Y. Wu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise - Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, KDD '96 CURE : An Efficient Clustering Algorithm for Large Databases – S. Guha, R. Rastogi and K. Shim, 1998. Introduction to Clustering Techniques – by Leo Wanner A comprehensive overview of Basic Clustering Algorithms – Glenn Fung Introduction to Data Mining – Tan/Steinbach/Kumar
- 31. Thanks! Presenters − Vasanth Prabhu Sundararaj − Gnana Sundar Rajendiran − Joyesh Mishra Source www.cise.ufl.edu/~jmishra/clustering Tools Used JDK 1.6, Eclipse, MATLAB, LABView, GnuPlot This slide was made using Open Office 2.2.1