An Effective Cluster-Aware Labeling Method for Web Search Results Using Concordant Document Frequencies
Masafumi Matsuhara,
Toshihiro Yoshida
Issue:
Volume 3, Issue 1, February 2014
Pages:
1-7
Received:
19 January 2014
Published:
20 February 2014
Abstract: In recent years, the amount of information on World Wide Web has exploded. Search engines are generally used for web searching; however, robot-type search engines have a few problems. One such problem is that it is difficult for a user to come up with an appropriate query for obtaining the search results she/he intends. Moreover, it is difficult for users to understand the contents of search results because a robot-type search engine outputs many search results in a long list format. To solve these problems, many methods have been proposed that classify the results of a robot-type search engine into clusters that are labeled and then shown to the user. To be effective, the cluster label needs to consist of appropriate words to describe the web sites within the cluster. In this study, we propose a labeling method using concordant document frequencies where the web search results of a query are classified into clusters and we use our techniques to assign the proper labels to those clusters. We then find the set of web sites that result from an AND-query using an original query word and the cluster label. If this set and the members of the cluster are common, we say that the concordant document frequency is high, and the cluster label is assigned a high weight. Thus, it is possible to assign an appropriate label using our proposed cluster-aware method. We demonstrate the effectiveness of our proposed method by simulation experiments.
Abstract: In recent years, the amount of information on World Wide Web has exploded. Search engines are generally used for web searching; however, robot-type search engines have a few problems. One such problem is that it is difficult for a user to come up with an appropriate query for obtaining the search results she/he intends. Moreover, it is difficult fo...
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Processing Overlapped Cells Using K-Means and Watershed
Faten Faraj Abushmmala,
Fadwa Faraj Abushmmala
Issue:
Volume 3, Issue 1, February 2014
Pages:
8-12
Received:
30 April 2014
Accepted:
17 May 2014
Published:
30 May 2014
Abstract: Processing overlapped cells are tricky process especially when an automatic computerized system deals with 2D images of cells needed to be processed in biomedical filed, if these cells are overlapped this might give the impression and wrong indication of abnormality presence. In this paper a methodology are suggested and implemented to separate the overlapped from non-overlapped cells giving as a result two groups (clusters) for each. And we try to give an estimation of numbers of cells that overlapped under the microscope, the success rates of separating the two clusters (overlapped and non overlapped cells) are 100% while the success rate of the estimating the number of correct cells that overlapped compared with medical personal point view are 79.3%.
Abstract: Processing overlapped cells are tricky process especially when an automatic computerized system deals with 2D images of cells needed to be processed in biomedical filed, if these cells are overlapped this might give the impression and wrong indication of abnormality presence. In this paper a methodology are suggested and implemented to separate th...
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