Abstract:Large, sparse categorical data is a natural way to represent complex data like sequences, trees, and graphs. Such data is prevalent in many applications, e.g., Criteo released a terabyte size click log data of 4 billion records with millions of dimensions. While most existing clustering algorithms like k-Means work well on dense, numerical data, there exist relatively few algorithms that can cluster sets of sparse categorical features.
In this paper, we propose a new method called k-FreqItems that pe… Show more
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