ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Clustering Functional Data Stream: Theory and Application for the Analysis of Web Data

12 September 2017, 12:10 – 12:40

Abstract

Submitted by
Tonio Di Battista
Authors
Tonio Di Battista (Università G. d'Annunzio), Fabrizio Maturo (Università G. d'Annunzio), Francesca Fortuna (Università G. d'Annunzio)
Abstract
In the last decades, the analysis of web data has become crucial for many companies. Most enterprises have a website, and to analyze consumer behaviour, when they surf the web, is crucial for making or changing corporate strategies. Information about the number of visits, bounce rate, time on site, traffic sources, and success of an online advertising are essential for understanding if and where firms are losing customers, forecast future sales, understand past performance, analyze profile customer behaviour, monitor buying patterns, measure the impact of site changes, and remove barriers to sale.
Therefore, it is widely recognized that discovering how to analyze strategic aspects of a website, finding a marketing strategy, and understanding standard visitors are very relevant topics both in business administration and statistics.
Analyzing this type of data often means to deal with big data and data streaming. The main issues in analyzing web traffic and web data are that they often flow continuously from a source and are potentially unbounded in size, and these circumstances inhibit to store the whole dataset. Moreover, the results of the inspection of this kind of data is useful when the analytics are in "real time" because, in several fields, the value of information decreases with time. Moreover, the analyst often cannot reanalyze the data after it is streamed, thus, it is important to use appropriate tools.
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.
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