fulltext.study @t Gmail

SNCStream+: Extending a high quality true anytime data stream clustering algorithm

Paper ID Volume ID Publish Year Pages File Format Full-Text
396462 670346 2016 14 PDF Available
Title
SNCStream+: Extending a high quality true anytime data stream clustering algorithm
Abstract

•SNCStream+ presents high clustering quality accordingly to the Cluster Mapping Measure.•SNCStream+ possesses diminished computational complexity when compared to its ancestor.•SNCStream+ is able to diminish the impact of the curse of dimensionality through the usage of specific distance metrics.

Data Stream Clustering is an active area of research which requires efficient algorithms capable of finding and updating clusters incrementally as data arrives. On top of that, due to the inherent evolving nature of data streams, it is expected that algorithms undergo both concept drifts and evolutions, which must be taken into account by the clustering algorithm, allowing incremental clustering updates. In this paper we present the Social Network Clusterer Stream+ (SNCStream+). SNCStream+ tackles the data stream clustering problem as a network formation and evolution problem, where instances and micro-clusters form clusters based on homophily. Our proposal has its parameters analyzed and it is evaluated in a broad set of problems against literature baselines. Results show that SNCStream+ achieves superior clustering quality (CMM), and feasible processing time and memory space usage when compared to the original SNCStream and other proposals of the literature.

Keywords
Data stream clustering; Unsupervised learning; Social networks theory
First Page Preview
SNCStream+: Extending a high quality true anytime data stream clustering algorithm
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us
Publisher
Database: Elsevier - ScienceDirect
Journal: Information Systems - Volume 62, December 2016, Pages 60–73
Authors
, , , ,
Subjects
Physical Sciences and Engineering Computer Science Artificial Intelligence
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us