Stream Clustering of Growing Objects

Authors:
Zaigham Faraz Siddiqui;Myra Spiliopoulou
Affiliations:
Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany 39106;Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany 39106
Venue:
DS '09 Proceedings of the 12th International Conference on Discovery Science
Year:
2009

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Abstract

We study incremental clustering of objects that grow and accumulate over time. The objects come from a multi-table stream e.g. streams of Customer and Transaction . As the Transactions stream accumulates, the Customers' profiles grow . First, we use an incremental propositionalisation to convert the multi-table stream into a single-table stream upon which we apply clustering. For this purpose, we develop an online version of K-Means algorithm that can handle these swelling objects and any new objects that arrive. The algorithm also monitors the quality of the model and performs re-clustering when it deteriorates. We evaluate our method on the PKDD Challenge 1999 dataset.