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Naganathan, E. R.
- Personalization in Web Usage Mining Using Neuro-Fuzzy Methods
Authors
1 Department of Computer Science, Sriram College of Arts & Science, Perumalpattu, IN
2 Department of CSE, Hindustan University, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 10 (2015), Pagination: 352-357Abstract
This paper presents implementation of Neuro-Fuzzy methods for improving web personalization. The objective of this work is to predict the next useful page for the user based on his previous visits in the website. Due to increase in the number of users on a particular web page, there is continued research going to cater to the users next expected web page. This increases the business for the website launcher. Now a days lots of online transactions are performed in the form of purchase of new products, selling of second hands products. Personalization helps inexperienced users to make transactions quickly with less difficulty. Many methods have evolved over the period to improve personalization of web usage. Existing artificial neural network (ANN) algorithms are combined with Fuzzy logic to form Neuro-Fuzzy logic methods for improving the performance of personalization of web usage.Keywords
Artificial Neural Network, Web Server Log, Web Usage Mining, Data Mining, E-Learning, User Access Patterns.
- Graphgain:A Proposed Measure for Ranking Mined Subgraph
Authors
1 Department of Computer Applications, Velammal College of Management and Computer Studies, Ambattur-Redhills Road, Chennai – 600066 Tamil Nadu, IN
2 Dept. of Computer Sci. & Engg., Alagappa University, Karaikudi-630003, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 7 (2010), Pagination: 135-139Abstract
Frequent itemset discovery algorithms have been used to solve various interesting problems over the year. As data mining techniques are being introduced and widely applied to non-traditional itemsets, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of finding subgraphs that occur frequently over the entire set of graphs. Modeling objects using graphs allows us to represne tarbitrary relations among entities. In this paper we present a computationally efficient algorithm for finding the ranking of such frequent subgraphs. The subgraph finding method may follow any one of the existing algorithm. In order to find out the ranking of subgraphs we present a new method called “graphgain”. A graphgain is the normalization technique applied at each position for a chosen value of Discounted Cumulative Gain (DCG) of a subgraph. The DCG alone cannot be used to achieve the performance from one query to the next in the search engine’s algorithm. To obtain the graphgain an ideal ordering of DCG (IDCG) at each position is to be found out. For this, a Modified Dicounted Cumulative Gain using “lift” is introduced here and IDCG is also evaluated. Then the graphgain is evaluated. Finally, the graphgain for all rules can be averaged to obtain a measure of the average performance of a search engine’s ranking algorithm. And also the ordering of graphgain will provide the order of evaluation of rules which gives in turn the efficient ranking of subgraph process.