應用於廣泛網路應用之資訊勘測

Abstract

A pervasive web application is a server providingmany web services for its registered users. Nowadays, three of basic services that a typical pervasive web application offers are membership management, search service and map-enabled photo service. In this thesis, we design a data mining framework composed of three different data mining techniques to improve the performance of three services. In order to improve the performance of membership management, in the second chapter, we develop a categorical decision tree classifier to classify users efficiently. It noted that the data of user profiles has an unique phenomenon. Its characteristic is that few attributes of user profiles have higher information gains to distinguish users. By exploiting this characteristic that a traditional decision tree classifier does not consider, our designed classifier can reduce the execution time in generating a decision tree for user classification. As a result, the decision tree generated by our classifier can identify users efficiently for special marketing needs of an advertisement. For the improvement of a search service, in the third chapter, we propose a sequential web search algorithm that leverages the sequential queries issued by users to search the required information. Compared with previous works, our approach uses the additional feedback data on result pages of sequential queries where prior works only use feedback data of a query. Thus, our approach can provide a better ranking of result pages for sequential queries. For the efficiency of retrieving geotagged photos, in the fourth chapter, we design a clustering algorithm that incrementally clusters geotagged photos in accordance to thresholds of different scales. Compared with other applications, we show the photo clusters instead of all photos where the number of photo clusters is much less than that of all photos. As a result, the performance of map-enabled photo service is improved efficiently.