Algorithms for clustering data
Algorithms for clustering data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Clustering of heterogeneously typed data with soft computing - a case study
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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Clustering is a representative grouping process to find out hidden information and understand the characteristics of dataset to get a view of the further analysis. The concept of similarity and dissimilarity of objects is a fundamental decisive factor for clustering and the measure of them dominates the quality of results. When attributes of data are categorical, it is not simple to quantify the dissimilarity of data objects that have unimportant attributes or synonymous values. We suggest a new idea to quantify dissimilarity of objects by using distribution information of data correlated to each categorical value. Our method discovers intrinsic relationship of values and measures dissimilarity of objects effectively. Our approach does not couple with a clustering algorithm tightly and so can be applied various algorithms flexibly. Experiments on both synthetic and real datasets show propriety and effectiveness of this method. When our method is applied only to traditional clustering algorithms, the results are considerably improved than those of previous methods.