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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Weighting in k-Means Clustering
Machine Learning
Towards a robust fuzzy clustering
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Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Discovery of Extremely Low-Dimensional Clusters Using Semi-Supervised Projected Clustering
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Robust maximum entropy clustering algorithm with its labeling for outliers
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An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
A convergence theorem for the fuzzy subspace clustering (FSC) algorithm
Pattern Recognition
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Pattern Recognition Letters
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list
Information Sciences: an International Journal
An entropy weighting mixture model for subspace clustering of high-dimensional data
Pattern Recognition Letters
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
A fuzzy minimax clustering model and its applications
Information Sciences: an International Journal
A fuzzy subspace algorithm for clustering high dimensional data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Fuzzy Systems
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Model-Based Method for Projective Clustering
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Neural Networks
Multiple Kernel Fuzzy Clustering
IEEE Transactions on Fuzzy Systems
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As one of the most popular clustering techniques for high dimensional data, soft subspace clustering (SSC) algorithms have been receiving a great deal of attention in recent years. Unfortunately, most existing works do not cluster high dimensional sparse data and noisy data in an effective manner. In this study, a novel soft subspace clustering algorithm called PI-SSC is proposed. By introducing a partition index (PI) into the objective function, a novel soft subspace clustering algorithm that combines the concepts of hard and fuzzy clustering is proposed. Furthermore, the robust property of PI-SSC is analyzed from the viewpoint of @e-insensitive distance. A convergence theorem for PI-SSC is also established by applying Zangwill's convergence theorem. The results of the experiment demonstrate the effectiveness of the proposed algorithm in high dimensional sparse text data and noisy texture data.