Summarizing and Mining Streaming Data via a Functional Data Approach

In recent years, the analysis of data streams has become a challenging task since many applicative fields generate massive amount of data that are difficult to store and to analyze with traditional techniques. In this paper we propose a strategy to summarize pseudo periodic streaming data affected by noise and sampling problems, by means of functional profiles. It is a clustering strategy performed in a divide and conquer manner. In the on-line step, a set of summarization structures, collect statistical information on data. Starting from these, in the off-line step, the final clustering structure and the set of functional profiles are computed.