Co-Clustering Algorithm: Batch, Mini-Batch, and Online
Keywords:
Batch, mini-batch, incremental, online co-clustering .Abstract
Unlike traditional one-way K-means clustering,
co-clustering simultaneously cluster both data points and
features of a two-dimensional data matrix. It is a powerful data
analysis technique that can discover latent patterns hidden
within particular rows and columns. Accordingly, co-clustering
has been successfully applied to varied domains, including, but
not only limited to, text clustering, microarray analysis, speech
and video analysis, and natural language processing. Assuming
a whole data matrix is available, usual co-clustering algorithm
updates all row and column assignments in batch mode. In this
paper, we develop an online incremental co-clustering
algorithm that can update both row and column clustering
statistics on the fly only for each available data point; thus, the
proposed algorithm can handle stream data collected from
sensor networks or handheld devices. Characteristics among
batch, mini-batch, and online clustering and co-clustering
algorithms are discussed and future work is provided.
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