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
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on 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
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Feature-guided clustering of multi-dimensional flow cytometry datasets
Journal of Biomedical Informatics
Bregman bubble clustering: A robust framework for mining dense clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering Massive Text Data Streams by Semantic Smoothing Model
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery from Data (TKDD)
Online Evaluation of Patterns from Evolving Web Data Streams
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Text stream clustering algorithm based on adaptive feature selection
Expert Systems with Applications: An International Journal
Experimental study on fighters behaviors mining
Expert Systems with Applications: An International Journal
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparing clustering algorithms and their influence on the evolution of labeled clusters
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Sumblr: continuous summarization of evolving tweet streams
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Journal of Information Science
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
Evolving soft subspace clustering
Applied Soft Computing
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Clustering data streams has been a new research topic, recently emerged from many real data mining applications, and has attracted a lot of research attention. However, there is little work on clustering high-dimensional streaming text data. This paper combines an efficient online spherical k-means (OSKM) algorithm with an existing scalable clustering strategy to achieve fast and adaptive clustering of text streams. The OSKM algorithm modifies the spherical k-means (SPKM) algorithm, using online update (for cluster centroids) based on the well-known Winner-Take-All competitive learning. It has been shown to be as efficient as SPKM, but much superior in clustering quality. The scalable clustering strategy was previously developed to deal with very large databases that cannot fit into a limited memory and that are too expensive to read/scan multiple times. Using the strategy, one keeps only sufficient statistics for history data to retain (part of) the contribution of history data and to accommodate the limited memory. To make the proposed clustering algorithm adaptive to data streams, we introduce a forgetting factor that applies exponential decay to the importance of history data. The older a set of text documents, the less weight they carry. Our experimental results demonstrate the efficiency of the proposed algorithm and reveal an intuitive and an interesting fact for clustering text streams-one needs to forget to be adaptive.