Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream

Authors:
Basheer Hawwash;Olfa Nasraoui
Affiliations:
University of Louisville, Louisville, KY;University of Louisville, Louisville, KY
Venue:
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Year:
2012

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Abstract

Clustering data streams is a challenging problem that has received significant attention in the recent decade. In this paper, we address the hitherto inadequately addressed challenge of managing the output of stream clustering. This task comprises the continuous cluster model validation, monitoring, trend and change detection, and summarization of the cluster mining output. For this purpose, we propose a complete framework to mine, track and validate the clusters in a data stream. The proposed framework keeps track of each discovered cluster model of the stream through time, while quantifying, modeling and summarizing the cluster evolution trends, and storing a summary of the cluster models and the evolution trends only at milestones corresponding to the occurrence of significant changes. Our experiments demonstrate the accuracy of the proposed framework in tracking cluster evolution over time, while generating a concise summary of the evolution trends over the lifetime of the stream.