Editorial: Data mining for understanding user needs

Sherry Y. Chen, Robert D. MacRedie, Xiaohui Liu, Alistair Sutcliffe

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

The editorial section of ACM Transactions on Computer-Human Interaction deals with data mining for understanding user needs. Data mining is the process of extracting valuable information from large amounts of data. It identifies hidden relationships, patterns, and interdependencies without leading to a priori hypotheses so predictive rules can be generated and interesting hypotheses found. Data mining has been used in mobile and collaborative applications to analyze users' behavior, demonstrating the way feedback produced by such analysis can change people's behaviors in meetings. Complex social behaviors can be known from analysis of simple data streams, such as recording conversational turns and locations recorded from mobile phones, or RF tags. Interaction explanation and feedback are also important so that people can understand how and why the system created its advice, while data mining can produce useful advice and recommendations for users.

Original languageEnglish
Article number1
JournalACM Transactions on Computer-Human Interaction
Volume17
Issue number1
DOIs
StatePublished - 1 Mar 2010

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