CUDAP: A Novel Clustering Algorithm for Uncertain Data Based on Approximate Backbone

Provided by: Academy Publisher
Topic: Big Data
Format: PDF
In recent years, data analysis and knowledge discovery in uncertain data become more and more important in many applications, such as, sensor network, biomedical measurement, financial market analysis and weather predictions, etc. Clustering for uncertain data is an interesting research topic in data mining. Researchers prefer to define uncertain data clustering problem by using combinatorial optimization model. Heuristic clustering algorithm is an efficient way to deal with this kind of clustering problem, but initialization sensitivity is one of inevitable drawbacks.

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