SE-Stream: Dimension Projection for Evolution-Based Clustering of High Dimensional Data Streams

Rattanapong Chairukwattana; Thanapat Kangkachit; Thanawin Rakthanmanon; Kitsana Waiyamai

doi:10.1007/978-3-319-02821-7_32

ScienceGate Book Chapters

BOOK-CHAPTER

SE-Stream: Dimension Projection for Evolution-Based Clustering of High Dimensional Data Streams

Rattanapong Chairukwattana Thanapat Kangkachit Thanawin Rakthanmanon Kitsana Waiyamai

Year: 2013 Advances in intelligent systems and computing Pages: 365-376 Publisher: Springer Nature

DOI: 10.1007/978-3-319-02821-7_32

Get Full-Text PDF Get Analytical Report

Keywords:

Cluster analysis Data stream clustering Computer science Merge (version control) Data stream mining Clustering high-dimensional data Data stream Data mining Correlation clustering CURE data clustering algorithm Artificial intelligence Parallel computing

Metrics

Cited By

1.98

FWCI (Field Weighted Citation Impact)

Refs

0.87

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Clustering Algorithms Research

Physical Sciences → Computer Science → Artificial Intelligence

Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Evolutionary Algorithms and Applications

Physical Sciences → Computer Science → Artificial Intelligence

SE-Stream: Dimension Projection for Evolution-Based Clustering of High Dimensional Data Streams

Metrics

Citation History

Topics

Related Documents

Efficient evolution-based clustering of high dimensional data streams with dimension projection

SED-Stream: discriminative dimension selection for evolution-based clustering of high dimensional data streams

High Dimensional Data Stream Clustering Algorithm Based on Random Projection

Clustering over High-Dimensional Data Streams Based on Grid Density and Effective Dimension

Constraint-based discriminative dimension selection for high-dimensional stream clustering