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IEEE Transactions on Knowledge and Data Engineering
An Automated Integration Approach for Semi-Structured and Structured Data
CODAS '01 Proceedings of the Third International Symposium on Cooperative Database Systems for Advanced Applications
Visualizing Real Estate Property Information on the Web
IV '99 Proceedings of the 1999 International Conference on Information Visualisation
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WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
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ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
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ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
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ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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World Wide Web (WWW) is a vast source of information, the problem of information overload is more acute than ever. Due to noise in WWW, it is becoming hard to find usable information. Real-estate listings are frequently available through different real estate agencies and published on different web sites. As a consequence, differences in price and description can also be observed. At the same time, a potential buyer or renter may prefer to get the entire description of a property of interest based on the data available on different portals and if possible track the changes in price. This problem can be considered as an illustration of a wider class of problems with integrating the data from numerous semistructured web data sources. The paper investigates the way clustering algorithms can be used to identify individual real estate properties described on different portals. Clustering algorithms have been used to group the records acquired from different web sources. Both standard clustering methods have been evaluated, and a method using new distance function combining similarity of semi-structured and unstructured data has been proposed. The latter approach has allowed substantial improvement in clustering results.