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Hebbian principal component clustering for information retrieval on a crowdsourcing platform


Niederberger, Thomas; Stoop, Norbert; Christen, Markus; Ott, Thomas (2012). Hebbian principal component clustering for information retrieval on a crowdsourcing platform. In: Nonlinear Dynamics of Electronic Systems, Wolfenbüttel, Germany, 11 July 2012 - 13 July 2012, 1-4.

Abstract

Crowdsourcing, a distributed process that involves outsourcing tasks to a network of people, is increasingly used by companies for generating solutions to problems of various kinds. In this way, thousands of people contribute a large amount of text data that needs to already be structured during the process of idea generation in order to avoid repetitions and to maximize the solution space. This is a hard information retrieval problem as the texts are very short and have little predefined structure. We present a solution that involves three steps: text data preprocessing, clustering, and visualization. In this contribution, we focus on clustering and visualization by presenting a Hebbian network approach that is able to learn the principal components of the data while the data set is continuously growing in size. We compare our approach to standard clustering applications and demonstrate its superiority with respect to classification reliability on a real-world example.

Abstract

Crowdsourcing, a distributed process that involves outsourcing tasks to a network of people, is increasingly used by companies for generating solutions to problems of various kinds. In this way, thousands of people contribute a large amount of text data that needs to already be structured during the process of idea generation in order to avoid repetitions and to maximize the solution space. This is a hard information retrieval problem as the texts are very short and have little predefined structure. We present a solution that involves three steps: text data preprocessing, clustering, and visualization. In this contribution, we focus on clustering and visualization by presenting a Hebbian network approach that is able to learn the principal components of the data while the data set is continuously growing in size. We compare our approach to standard clustering applications and demonstrate its superiority with respect to classification reliability on a real-world example.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Ethics and History of Medicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Event End Date:13 July 2012
Deposited On:04 Feb 2013 10:23
Last Modified:07 Dec 2017 18:44
Publisher:IEEE
ISBN:978-3-8007-3444-3
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Official URL:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6293769
Related URLs:http://www.ndes2012.org/ (Organisation)

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