Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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Clustering Data Streams via Functional Data Analysis: a Comparison between Hierarchical Clustering and K-means Approaches
Fabrizio Maturo, Francesco Fortuna, Tonio Di Battista

Last modified: 2018-09-13


Recently, the analysis of web data, has become essential in many researchfields. For example, for a large number of companies, corporate strategies shouldbe based on the analysis of customer behaviour in surfing the world wide web. Themain issues in analysing web traffic and web data are that they often flow continuouslyfrom a source and are potentially unbounded in size, and these circumstancesinhibit to store the whole dataset. In this paper, we propose an alternative clusteringfunctional data stream method to implement existing techniques, and we addressphenomena in which web data are expressed by a curve or a function. In particular,we deal with a specific type of web data, i.e. trends of google queries. Specifically,focusing on top football players data, we compare the functional k-meansapproach to the functional Hierarchical Clustering for detecting specific pattern ofsearch trends over time.

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