Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Clustering and singular value decomposition for approximate indexing in high dimensional spaces
Proceedings of the seventh international conference on Information and knowledge management
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Unsupervised feature selection for text data
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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In this paper we first propose a global unsupervised feature selection approach for text, based on frequent itemset mining. As a result, each document is represented as a set of words that co-occur frequently in the given corpus of documents. We then introduce a locally adaptive clustering algorithm, designed to estimate (local) word relevance and, simultaneously, to group the documents. We present experimental results to demonstrate the feasibility of our approach. Furthermore, the analysis of the weights credited to terms provides evidence that the identified keywords can guide the process of label assignment to clusters. We take into consideration both spam email filtering and general classification datasets. Our analysis of the distribution of weights in the two cases provides insights on how the spam problem distinguishes from the general classification case.