An Effective Clustering Algorithm for Transaction Databases Based on K-Mean

Provided by: Academy Publisher
Topic: Big Data
Format: PDF
Clustering is an important technique in machine learning, which has been successfully applied in many applications such as text and webpage classifications, but less in transaction database classification. A large organization usually has many branches and accumulates a huge amount of data in their branch databases called multi-databases. At present, the best way of mining multi-databases is, first, to classify them into different classes. In this paper, the authors redefine related concepts of transaction database clustering, and then in connection to the traditional clustering method, they propose a strategy of clustering transaction databases based on the k-mean.

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