Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 6 of 10
Back to Result List

Automated feature construction for classification of time ordered data sequences

  • The recent years and especially the Internet have changed the ways in which data is stored. It is now common to store data in the form of transactions, together with ist creation time-stamp. These transactions can often be attributed to Logical units, e.g., all transactions that belong to one customer. These groups, we refer to them as data sequences, have a more complex structure than tuple-based data. This makes it more difficult to find discriminatory patterns for classification purposes. However, the complex structure potentially enables us to track behaviour and its change over the course of 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, Neural Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations are required 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 binary classification tasks.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar


Name:Laux, Friedrich
Erschienen in:International Journal on Advances in Software
Document Type:Article
Year of Publication:2014
Creating Corporation:International Academy, Research, and Industry Association (IARIA)
Tag:feature construction; sequential data; temporal data mining
Issue:3 & 4
First Page:632
Last Page:641
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft
Open Access:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell, Weitergabe unter gleichen Bedingungen