A weightless neural network-based approach for stream data clustering

Authors:
Douglas Cardoso;Massimo De Gregorio;Priscila Lima;João Gama;Felipe França
Affiliations:
Universidade Federal do Rio de Janeiro, PESC-COPPE, Brasil;Istituto di Cibernetica "E. Caianiello" - CNR, Pozzuoli, NA, Italy;DEMAT-ICE, Universidade Federal Rural do Rio de Janeiro, Brasil;LIAAD-INESC, Universidade do Porto, Portugal;Universidade Federal do Rio de Janeiro, PESC-COPPE, Brasil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 8
Cited 0

Proceedings of the twenty-first international conference on Machine learning

ICML '04 Proceedings of the twenty-first international conference on Machine learning
A framework for clustering evolving data streams

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Robust projected clustering

Knowledge and Information Systems
Tracking clusters in evolving data streams over sliding windows

Knowledge and Information Systems
Incremental clustering of dynamic data streams using connectivity based representative points

Data & Knowledge Engineering
Density-based clustering of data streams at multiple resolutions

ACM Transactions on Knowledge Discovery from Data (TKDD)
MOA: a real-time analytics open source framework

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
The ClusTree: indexing micro-clusters for anytime stream mining

Knowledge and Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the major data mining tasks is to cluster similar data, because of its usefulness, providing means of summarizing large ammounts of raw data into handy information. Clustering data streams is particularly challenging, because of the constraints imposed when dealing with this kind of input. Here we report our work, in which it was investigated the use of WiSARD discriminators as primary data synthesizing units. An analysis of StreamWiSARD, a new sliding-window stream data clustering system, the benefits and the drawbacks of its use and a comparison to other approaches are all presented.