Hamming Distance based Clustering Algorithm

Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data.As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data.

[1]  Jon M. Kleinberg,et al.  Clustering categorical data: an approach based on dynamical systems , 2000, The VLDB Journal.

[2]  Carla E. Brodley,et al.  Feature Selection for Unsupervised Learning , 2004, J. Mach. Learn. Res..

[3]  Sudipto Guha,et al.  ROCK: A Robust Clustering Algorithm for Categorical Attributes , 2000, Inf. Syst..

[4]  Zhang Yi,et al.  Clustering Categorical Data , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[5]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[6]  Bin Dong,et al.  K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset , 2005, ArXiv.

[7]  Ke Wang,et al.  Clustering transactions using large items , 1999, CIKM '99.

[8]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[9]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[10]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[11]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[12]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[13]  Aidong Zhang,et al.  WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases , 1998, VLDB.

[14]  He Zengyou,et al.  Squeezer: an efficient algorithm for clustering categorical data , 2002 .

[15]  P. Song,et al.  Clustering Categorical Data Based on Distance Vectors , 2006 .

[16]  Johannes Gehrke,et al.  CACTUS—clustering categorical data using summaries , 1999, KDD '99.

[17]  Yannis Manolopoulos,et al.  C2P: Clustering based on Closest Pairs , 2001, VLDB.

[18]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[19]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[20]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[21]  Joshua Zhexue Huang,et al.  A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining , 1997, DMKD.

[22]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .