Informatics Publishing Limited

V. Chitraa ¹, Antony Selvadoss Thanamani ²

Affiliations
1 CMS College of Science & Commerce (Autonomous), Coimbatore, Tamilnadu, IN
2 Computer Science, NGM College (Autonomous), Pollachi, Coimbatore, Tamilnadu, IN

Abstract

Objectives: The primary objective of this research paper is to design a new and efficient clustering technique to group user navigation patterns which are useful for classification system to classify a new user with the previous users group. Methodology: Three real time web log data sets are collected from e-commerce web server, academic institution web server and a research journal web server. All three sets were collected from IIS web servers. After navigation patterns are derived from preprocessing step it is clustered into groups by using traditional Fuzzy C-Means technique. The clusters are validated and re-clustered using Bolzano_Weierstrass Theorem. Findings: Web log data is preprocessed and ICA is applied in the user session matrix to select relevant and important features. To measure the clustering accuracy of proposed and the existing methods, the parameters such as Rand Index, F measure are calculated and compared. It shows proposed BWFCM have higher rand index rate than FCM and lesser error rate. To understand the impact of the feature selection method, the data sets were implemented with the existing and proposed methods of feature selection. The parameters taken for comparison were Rand Index, Sum of Squared Errors, F-measure. The method was implemented in all the three data sets after data cleaning, session construction step. Clustering was carried out twice with the proposed clustering algorithm in all the three data sets, without selecting features and after selecting features. It was observed that the clustering results are poor when applied in full data set with irrelevant features, and the performance was increased after relevant features were selected. Conclusion: The result of the optimized clustering proves its significance and there is an increase in similarity of intra clustering and dissimilarity in inter clustering than the existing methods.

Keywords

Bolzano_Weierstrass Theorem, Clustering, Feature Selection, Navigation Patterns, Web Usage Mining

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G. Maria Priscilla ¹, Antony Selvadoss Thanamani ²

Affiliations
1 Dept of Comp. Sci, SNR Sons College, Coimbatore, IN
2 NGM College, Pollachi, IN

Abstract

Internet the growing technology with the emergence of new applications and diverse quality of service requirements has become increasingly important. Congestion the major problem in internet requires some management in responsive and unresponsive flows. Active Queue Management (AQM) schemes are a class of queue management algorithms which are designed to overcome the drawbacks in the classical drop-tail queues and for congestion avoidance and congestion prevention. These AQM schemes are classified based on the decisions taken while dropping the packets. The types of marking or dropping the packets to improve the throughput , performance , fairness in bursty traffics and the packet loss ratio are involved. The reactive congestion control active queue management schemes are discussed in this paper.

Keywords

Congestion Control, AQM, RED, Queue Management.

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K. Sashi ¹, Antony Selvadoss Thanamani ²

Affiliations
1 Dept. of Comp Science, S.N.R Sons College, Coimbatore, IN
2 NGM College, Pollachi, IS

Abstract

Data Grid deals with data intensive applications in scientific and enterprise computing. These applications handle large data sets that need to be transferred and replicated among different grid sites. Data replication is an important technique for data availability and data access. Data should be replicated in an automatic way to make the system more reliable, accessible and less sensitive to subsystem failures. Replication allows faster access to files and increases the job execution performance. This paper provides a survey on various replication algorithms, some of its merits and demerits.

Keywords

Grid Computing, Data Application, Data Grid, Data Replication.

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K. Jothimani ¹, Antony Selvadoss Thanamani ¹

Affiliations
1 Department of Computer Science, NGM College, Pollachi, IN

Abstract

The most significant tasks in data mining are the process of mining frequent itemsets over data streams. It should support the flexible trade-off between processing time and mining accuracy. The objective was to propose an effective algorithm which generates frequent itemsets in a very less time by avoiding multiple scans. In this paper, we present an improved algorithm ESWCA for mining frequent itemsets using sliding window model. The ESWCA algorithm processes on an on-line transactional data stream. In this approach, we handle continues transaction slides in a segment-based manner which produces the improved runtime and memory consumption. Also, by revising the fair-cutter in the novel algorithm, multiple scans of the entire datasets will be avoided. Our experiments show that our algorithm not only achieved effectively consumes less memory, but also runs in an efficient manner.

Keywords

Data Stream, Data-Stream Mining, Frequent Itemset, and Sliding Window.

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A. Kutralam ¹, Antony Selvadoss Thanamani ²

Affiliations
1 Department of Computer Applications, J.J. College of Arts and Science, Pudukkottai, Tamilnadu, IN
2 Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu, IN

Abstract

Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data increasing every year, data mining is becoming an increasingly important tool to transform this data into information. We focus on APRIORI algorithm, a popular data mining technique and analyze the performance of linked list based implementation as a basis for mining frequent item sequences in a transactional database. This algorithm has given us new capabilities to identify associations in large data sets. But an important issue, still not sufficiently scanned, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. We work with some association rule hiding algorithms and examine their performances in order to analyze their time complexity and the impact that they have in the original database. We work a side effect – the number of new rules generated during the hiding process.

Keywords

Association Rule Mining, Apriori Algorithm, Privacy Issues, Hiding Strategies.

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G. Marimuthu ¹, Antony Selvadoss Thanamani ²

Affiliations
1 Department of Computer Science, Yadava College, Madurai, IN
2 Department of Computer Science, NGM College, Coimbatore, IN

Abstract

Knowledge Management System refers to a system for managing knowledge in organizations, supporting creation, capture, storage and dissemination of information. It can comprise a part of a knowledge Management initiative. Knowledge Management can be regarded as a form of representation at the results of action and reflection in Explicit form Since the concept of the Industry cluster was popularized by Porter in 1990, many countries try to improve the competitiveness through Industry sector. Not only companies who take part in the cluster but also academic institutes, government agencies, associations and supportive industries. The more actors involved in the cluster the more knowledge were distributed among member of cluster. Although many literatures about cluster explained how knowledge is important for the cluster development. But, there is no specific knowledge management methodology or system for the cluster. This study is concerned about knowledge exchange in the cluster by using knowledge engineering methodology to analyze, model and design Knowledge Management System (KMS). At the end of this study, KMS will be studied the possibilities of implementing in the handicraft cluster as our case study. This paper present methodology and primary result form knowledge engineering. Then the KMS architecture was proposed as the result of the preliminary study in this paper.

Keywords

Knowledge Management, Architecture, Web Mining, Industry Cluster.

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