Clustering Data Streams via Functional Data Analysis: a Comparison between Hierarchical Clustering and K-means Approaches

Recently, the analysis of web data, has become essential in many research fields. For example, for a large number of companies, corporate strategies should be based on the analysis of customer behaviour in surfing the world wide web. The main issues in analysing web traffic and web data are that they often flow continu ously from a source and are potentially unbounded in size, and these circumstances inhibit to store the whole dataset. In this paper, we propose an alternative clustering functional data stream method to implement existing techniques, and we address phenomena in which web data are expressed by a curve or a function. In particu- lar, 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-means approach to the functional Hierarchical Clustering for detecting specific pattern of search trends over time.