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
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Future Generation Computer Systems - Special double issue on data mining
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SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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SIGMOD '00 Proceedings of the 2000 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
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VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Feature Weighting in k-Means Clustering
Machine Learning
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
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Computational Statistics & Data Analysis
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Proceedings of the VLDB Endowment
ACM Transactions on Knowledge Discovery from Data (TKDD)
A general framework for subspace detection in unordered multidimensional data
Pattern Recognition
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Expert Systems with Applications: An International Journal
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Intelligent Data Analysis
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This paper proposes a new method to weight subspaces in feature groups and individual features for clustering high-dimensional data. In this method, the features of high-dimensional data are divided into feature groups, based on their natural characteristics. Two types of weights are introduced to the clustering process to simultaneously identify the importance of feature groups and individual features in each cluster. A new optimization model is given to define the optimization process and a new clustering algorithm FG-k-means is proposed to optimize the optimization model. The new algorithm is an extension to k-means by adding two additional steps to automatically calculate the two types of subspace weights. A new data generation method is presented to generate high-dimensional data with clusters in subspaces of both feature groups and individual features. Experimental results on synthetic and real-life data have shown that the FG-k-means algorithm significantly outperformed four k-means type algorithms, i.e., k-means, W-k-means, LAC and EWKM in almost all experiments. The new algorithm is robust to noise and missing values which commonly exist in high-dimensional data.