Current Trends in Information Technology
Volume 1, Issue 2-3 (2011)
Published
Table of contents
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A Comparison Between Two Association Rule Mining Techniques
Abstract: Normal 0 false false false EN-US X-NONE X-NONE Data mining requires associated items to be mined from given transactions. Association rule mining (ARM) has gained importance in view of trend-prediction and decision-making process. Mostly ARM is implemented using variants of FP tree growth techniques. This paper introduces an improved technique for ARM – Improved Relative Dotted Sequence Path (IRDSP)-based ARM. The logic justifying why this technique saves time in comparison …
Published in Current Trends in Information Technology · Vol. 1, Issue 2-3, 2011 · pp. 1–7 Read article
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Performance Improvements in Thin Client Through Software Fault Tolerance
Abstract: Normal 0 false false false EN-US X-NONE X-NONE We have already moved towards hi-tech technology in each field of study in a cost effective manner. Thin client technology boasts of an impressive range of financial, technical, and administrative benefits combined with virtualization technology, higher bandwidth availability, and cheaper and high performance processors. In thin client systems, the server must function with a very high availability even under any fault condition. …
Published in Current Trends in Information Technology · Vol. 1, Issue 2-3, 2011 · pp. 16–21 Read article
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Sentiment Extraction and Analysis of Product Reviews at Sentence Level
Abstract: 1024x768 Normal 0 false false false EN-US X-NONE X-NONE These days large number of people are using internet as a medium to communicate and express their views on politics, products, movies and other various domains. Monitoring these views online will be helpful for the companies, individuals or groups, who are interested in knowing what the customers are talking about their products. But manually monitoring is a costly and time-consuming task. …
Published in Current Trends in Information Technology · Vol. 1, Issue 2-3, 2011 · pp. 22–29 Read article
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Clustering Categorical Data Using Node Importance Method
Abstract: Normal 0 false false false EN-US X-NONE X-NONE Data clustering is an important technique in data analysis. It can be defined as a given set of data objects, the problem of clustering is to partition data objects into groups in such a way that objects in same group are similar and objects in different groups are dissimilar according to the predefined similarity measurement [Han Jiawei and Kamber Micheline. Data mining: …
Published in Current Trends in Information Technology · Vol. 1, Issue 2-3, 2011 · pp. 8–15 Read article