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CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Journal of the ACM (JACM)
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Machine Learning
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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ACM Transactions on Database Systems (TODS)
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
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We present a novel spectral-based algorithm for clustering categorical data that combines attribute relationship and dimension reduction techniques found in Principal Component Analysis (PCA) and Latent Semantic Indexing (LSI). The new algorithm uses data summaries that consist of attribute occurrence and co-occurrence frequencies to create a set of vectors each of which represents a cluster. We refer to these vectors as "candidate cluster representatives." The algorithm also uses spectral decomposition of the data summaries matrix to project and cluster the data objects in a reduced space. We refer to the algorithm as SCCADDS (Spectral-based Clustering algorithm for CAtegorical Data using Data Summaries). SCCADDS differs from other spectral clustering algorithms in several key respects. First, the algorithm uses the attribute categories similarity matrix instead of the data object similarity matrix (as is the case with most spectral algorithms that find the normalized cut of a graph of nodes of data objects). SCCADDS scales well for large datasets since in most categorical clustering applications the number of attribute categories is small relative to the number of data objects. Second, non-recursive spectral-based clustering algorithms typically require K-means or some other iterative clustering method after the data objects have been projected into a reduced space. SCCADDS clusters the data objects directly by comparing them to candidate cluster representatives without the need for an iterative clustering method. Third, unlike standard spectral-based algorithms, the complexity of SCCADDS is linear in terms of the number of data objects. Results on datasets widely used to test categorical clustering algorithms show that SCCADDS produces clusters that are consistent with those produced by existing algorithms, while avoiding the computation of the spectra of large matrices and problems inherent in methods that employ the K-means type algorithms.