M. Krishnamurthy ¹, K. Manivannan ², A. Chilambuchelvan ³, E. Rajalakshmi ⁴, A. Kannan ⁵

Affiliations
1 CSE, KCG College of Technology, Chennai - 600097, Tamil Nadu, IN
2 IT, RMK Engineering College, Kavaraipettai, Thiruvallur District - 601206, Tamil Nadu, IN
3 RMK Engineering College, Kavaraipettai, Thiruvallur District - 601206, Tamil Nadu, IN
4 CSE, SVCE, Sriperumbudur - 602117, Tamil Nadu, IN
5 IST, Anna University, Chennai - 600025, Tamil Nadu, IN

Abstract

Frequent item sets is one of the most investigated fields of data mining. The significant feature is to find new techniques to reduce candidate item sets in order to generate frequent item sets efficiently. This paper introduces an efficient algorithm called Enhanced Candidate Generation for Frequent item set Generation (ECG for FIG) for finding frequent item sets from large databases. The existing algorithm for frequent item set generation scan the original database more than once, use more storage space, take more processing time. The proposed algorithm gives a solution to this by representing the transactions in the database with decimal numbers instead of binary values and strings. The original database is scanned only once and is converted into an equivalent decimal value to reduce the storage space. The subset generation concept is used to generate frequent item sets. Thus the proposed algorithm reduces the scanning time, processing time and the storage space respectively. When compared with the existing algorithms, the proposed algorithm takes very less execution time and memory. When implemented the algorithm using java and tested with WEKA tool, for 400 transactions of twenty five items, ECG for FIG is taking only 800 bytes of memory and 2000000000 ns (two seconds), whereas all the other above mentioned algorithms are taking 20800 bytes of memory and more than two seconds.

Keywords

Decimal Conversion, Frequent Item Set Generation, Redundancy Elimination, Storage Space Reduction

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K. Selvakumar ¹, L. Sai Ramesh ², A. Kannan ²

Affiliations
1 School of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
2 Department of Information Science and Technology, CEG, Anna University, Chennai - 600025, Tamil Nadu, IN

Abstract

To gain information about user interests in Web pages is needed to advance in Web security. An approach to pick up that information includes understanding the client's perusing conduct, examining the Web log records with the procedures of preprocessing and client clustering. Time spent on Web pages and the types of operations show the degree of a Web user's intention. The data set comprises of Web log files obtained by collecting the user logs during a six month period. A new enhanced K-means clustering algorithm proposed in this paper for grouping user based on their preferred Web content and their temporal constraints. The enhanced K-mean clustering calculates initial centroids instead of random choice and uses time intervals to heighten the security and performance. Utilizing this methodology, client access designs with comparable looking practices are assembled into a particular class amid a particular time interval. Also secured communication among the various users groups will be achieved through hill cipher technique.

Keywords

Preprocessing, Security and Hill Cipher, Temporal K-Means Algorithm, Web User Categorization.

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G. Saranya ¹, H. K. Nehemiah ¹, A. Kannan ², V. Pavithra ³

Affiliations
1 Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai - 600025, Tamil Nadu, IN
2 Information Science and Technology, College of Engineering Guindy, Anna University, Chennai - 600025,Tamil Nadu, IN
3 Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai - 600025, Tamil Nadu, IN

Abstract

Objective: Code smells indicate the design decay in software applications. The code smells existence in the software will hinder the understandability of code and possibly increases changes and fault proneness. Methods / Statistical Analysis: To remove the code smells’ from the software applications refactoring operations are applied which in turn improves the software system structure without changing its overall behaviour. Generally, in a large sized system, code smell cannot be fixed automatically. Therefore based on the maintainer’s preference, the prioritized list of refactoring sequences to fix the code smells is essential. Findings: Majority of the refactoring just rely on the structural information, which fails to preserve the construct semantics, minimization of changes and the use of development history. To overcome this, in this work, the Strength Pareto Evolutionary Algorithm (SPEA) is used to prioritize the list of refactoring operations that maximize the quality improvement, constructs semantics coherence and preserving the consistency with the previous refactoring. This work is carried out on two open source software Xerces-J and J Hot Draw. Blob, shotgun surgery, functional decomposition, data class, Swiss army knife and schizophrenic class code smells’ are considered for prioritizing refactoring operations in these open source system. SPEA is evaluated using the metrics Code smell Correction Ratio (CCR) and Refactoring Meanings (RM). Application / Improvements: SPEA is compared with other algorithms namely Non-dominated Sorting Genetic Algorithm II (NSGA II) and Chemical Reaction Optimization (CRO), to prove its efficiency in prioritizing code smell correction tasks.

Keywords

Code Smells, Maintenance, Prioritizing, Refactoring, Search Based Software Engineering, Strength Pareto Evolutionary Algorithm (SPEA)

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