Pub Date : 2012-05-01DOI: 10.20894/IJWT.104.001.001.004
Umar Sathic Ali, C. JothiVenkateswaran
With the rapid growth of online documents available on the World Wide Web necessitate the task of classifying those documents into semantic categories. Text categorization is the task of automatically classifying the textual documents into a set of predefined categories. In this paper, we report the empirical evaluation of lazy learning classifier such as kNN and its variant like distance weighted kNN and our newly proposed evident theoretic kNN for text categorization task over two benchmark datasets. We observed the superiority of evident theoretic kNN method over others in all experiments we conducted.
{"title":"An empirical evaluation of lazy learning classifiers for text categorization","authors":"Umar Sathic Ali, C. JothiVenkateswaran","doi":"10.20894/IJWT.104.001.001.004","DOIUrl":"https://doi.org/10.20894/IJWT.104.001.001.004","url":null,"abstract":"With the rapid growth of online documents available on the World Wide Web necessitate the task of classifying those documents into semantic categories. Text categorization is the task of automatically classifying the textual documents into a set of predefined categories. In this paper, we report the empirical evaluation of lazy learning classifier such as kNN and its variant like distance weighted kNN and our newly proposed evident theoretic kNN for text categorization task over two benchmark datasets. We observed the superiority of evident theoretic kNN method over others in all experiments we conducted.","PeriodicalId":132460,"journal":{"name":"Indian Journal of Education and Information Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-05-01DOI: 10.20894/IJWT.104.001.001.006
G. Sudhamathy, C. Venkateswaran
In this paper, we proposed an efficient approach for frequent pattern mining using web logs - web usage mining and we call this approach as HFPA. In our approach HFPA, the proposed technique is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. We applied this technique and compared its performance with that of classical Apriori-mined rules. The results indicate that the proposed approach HFPA not only generates far fewer rules than Apriori-based algorithms (FPA), but also generate rules of comparable quality with respect to three objective performance measures namely, Confidence, Lift and Conviction. Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper we have proposed effective pruning techniques that were characterized by the natural web link structures. Our experiments showed that interestingness measures can successfully be used to sort the discovered association rules after the pruning method was applied. Most of the rules that ranked highly according to the interestingness measures proved to be truly valuable to a web site administrator.
{"title":"Hierarchical frequent pattern analysis of web logs for efficient interestingness prediction","authors":"G. Sudhamathy, C. Venkateswaran","doi":"10.20894/IJWT.104.001.001.006","DOIUrl":"https://doi.org/10.20894/IJWT.104.001.001.006","url":null,"abstract":"In this paper, we proposed an efficient approach for frequent pattern mining using web logs - web usage mining and we call this approach as HFPA. In our approach HFPA, the proposed technique is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. We applied this technique and compared its performance with that of classical Apriori-mined rules. The results indicate that the proposed approach HFPA not only generates far fewer rules than Apriori-based algorithms (FPA), but also generate rules of comparable quality with respect to three objective performance measures namely, Confidence, Lift and Conviction. Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper we have proposed effective pruning techniques that were characterized by the natural web link structures. Our experiments showed that interestingness measures can successfully be used to sort the discovered association rules after the pruning method was applied. Most of the rules that ranked highly according to the interestingness measures proved to be truly valuable to a web site administrator.","PeriodicalId":132460,"journal":{"name":"Indian Journal of Education and Information Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.5958/2320-6233.2014.00006.6
F. Ahmadi, M. Najafi, Ali Khaneh-Keshi
This study investigated the relationship of academic self-efficacy and self-regulation with academic performance among the girl high school students with school refusal behavior and normal students. The sample of the study consists of 120 students (60 students with school refusal behavior and 60 normal students) which were selected by using a simple random sampling technique from 270 students who had been responded to the school refusal behavior scale 11. The data were collected with academic self-efficacy scale 17; self-regulation scale 22 and also the mean scores of the students in an academic term. The data was analyzed by Pearson’s moment coefficient of correlation, the Fisher-Z test, and multiple regressions. Findings showed that: 1) the relationship between academic self-efficacy and academic performance in two groups was positive and significant; 2) the relationship between self-regulation and academic performance in two groups was positive and significant; 3) the Fisher-Z test showed no significant difference between two groups regarding to the relationships of the variables to academic performance, 4) the multiple correlation coefficient of predictor variables with academic performance was significant; 5) self-regulation was found as a good predictor of academic performance in two groups.
{"title":"The Relationship of Academic Self-Efficacy and Self-regulation with Academic Performance among the High School Students with School Refusal Behavior and Normal Students","authors":"F. Ahmadi, M. Najafi, Ali Khaneh-Keshi","doi":"10.5958/2320-6233.2014.00006.6","DOIUrl":"https://doi.org/10.5958/2320-6233.2014.00006.6","url":null,"abstract":"This study investigated the relationship of academic self-efficacy and self-regulation with academic performance among the girl high school students with school refusal behavior and normal students. The sample of the study consists of 120 students (60 students with school refusal behavior and 60 normal students) which were selected by using a simple random sampling technique from 270 students who had been responded to the school refusal behavior scale 11. The data were collected with academic self-efficacy scale 17; self-regulation scale 22 and also the mean scores of the students in an academic term. The data was analyzed by Pearson’s moment coefficient of correlation, the Fisher-Z test, and multiple regressions. Findings showed that: 1) the relationship between academic self-efficacy and academic performance in two groups was positive and significant; 2) the relationship between self-regulation and academic performance in two groups was positive and significant; 3) the Fisher-Z test showed no significant difference between two groups regarding to the relationships of the variables to academic performance, 4) the multiple correlation coefficient of predictor variables with academic performance was significant; 5) self-regulation was found as a good predictor of academic performance in two groups.","PeriodicalId":132460,"journal":{"name":"Indian Journal of Education and Information Management","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130685843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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