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Feature construction for time ordered data sequences

  • The recent years and especially the Internet have changed the way on how data is stored. We now often store data together with its creation time-stamp. These data sequences potentially enable us to track the change of data over time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions, is still a challenging task for data miners. However, before Standard algorithms such as Decision Trees, Neuronal Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations need to be done in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the Feature construction step. The proposed algorithm is described in detail and applied on a real life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well known data mining algorithms for classification tasks.

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Author of HS ReutlingenLaux, Friedrich
Erschienen in:DBKDA 2013, the Fifth International Conference on Advances in Databases, Knowledge, and Data Applications : January 27 - February 1, 2013, Seville, Spain
Editor:Friedrich LauxORCiD, Andreas Schmidt, Kiyoshi Nitta, Iztok Savnik
Document Type:Conference proceeding
Publication year:2014
Creating Corporation:International Academy, Research, and Industry Association (IARIA)
Tag:feature construction; sequential data; temporal data mining
Page Number:6
First Page:1
Last Page:6
DDC classes:000 Allgemeines, Wissenschaft
Open access?:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell, Weitergabe unter gleichen Bedingungen