Competitive learning algorithms for vector quantization
Neural Networks
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Concept decompositions for large sparse text data using clustering
Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A unified framework for model-based clustering
The Journal of Machine Learning Research
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
2005 Special Issue: Efficient streaming text clustering
Neural Networks - 2005 Special issue: IJCNN 2005
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A convergence theorem for the fuzzy subspace clustering (FSC) algorithm
Pattern Recognition
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A Link-Based Approach to the Cluster Ensemble Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
Convergence of the Single-Pass and Online Fuzzy C-Means Algorithms
IEEE Transactions on Fuzzy Systems
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A key challenge to most conventional clustering algorithms in handling many real world problems is that, data points in different clusters are often correlated with different subsets of features. To address this problem, subspace clustering has attracted increasing attention in recent years. In practical data mining applications, data points may arrive in continuous streams with chunks of samples being collected at different time points. In addition, huge amounts of data often cannot be kept in the main memory due to memory restriction. Accordingly, a range of evolving clustering algorithms has been proposed, however, traditional evolving clustering methods cannot be effectively applied to large-scale high dimensional data and data streams. In this study, we extend the online learning strategy and scalable clustering technique to soft subspace clustering to form evolving soft subspace clustering. We propose two online soft subspace clustering algorithms, OFWSC and OEWSC, and two streaming soft subspace clustering algorithms, SSSC_F and SSSC_E. The proposed evolving soft subspace clustering leverages on the effectiveness of online learning scheme and scalable clustering methods for streaming data by revealing the important local subspace characteristics of high dimensional data. Substantial experimental results on both artificial and real-world datasets demonstrate that our proposed methods are generally effective in evolving clustering and achieve superior performance over existing soft subspace clustering techniques.