Extending k-Means-Based Algorithms for Evolving Data Streams with Variable Number of Clusters

• Published in: 2011 Tenth International Conference on Machine Learning and Applications Vol. 2; pp. 14 - 19
• Main Authors: de Andrade Silva, J., Hruschka, E. R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2011
• Subjects: Approximation algorithms; Clustering; Clustering algorithms; Data Stream; Heuristic algorithms; Indexes; Machine learning algorithms; Online Clustering; Partitioning algorithms; Prototypes
• ISBN: 9781457721342; 1457721341
• DOI: 10.1109/ICMLA.2011.67

Many algorithms for clustering data streams based on the widely used k-Means have been proposed in the literature. Most of them assume that the number of clusters, k, is known and fixed a priori by the user. Aimed at relaxing this assumption, which is often unrealistic in practical applications, we describe an algorithmic framework that allows estimating k automatically from data. We illustrate the potential of the proposed framework by using three state-of-the-art algorithms for clustering data streams - Stream LSearch, CluStream, and Stream KM++ - combined with two well-known algorithms for estimating the number of clusters, namely: Ordered Multiple Runs of k-Means (OMRk) and Bisecting k-Means (BkM). As an additional contribution, we experimentally compare the resulting algorithmic instantiations in both synthetic and real-world data streams. Analyses of statistical significance suggest that OMRk yields to the best data partitions, while BkM is more computationally efficient. Also, the combination of Stream KM++ with OMRk leads to the best trade-off between accuracy and efficiency.