Informatics Publishing Limited

E. Ramaraj ¹, N. Venkatesan

Affiliations
1 Madurai Kamaraj University, Madurai, Tamilnadu, IN

Abstract

The tremendous growth in data has generated the need for new techniques that can intelligently transform the massive data into useful information and knowledge. Data Mining is such a technique that extracts non-trivial, implicit, previously unknown and potentially useful information from the data in databases. Association Rule Mining is one of the most important and well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations of casual structures among sets of items in the transaction databases or other data repositories. Association rules are widely used in market databases, spatial databases, biological databases, medical databases and crime databases. This paper focuses a new algorithm to mine both positive and negative rules from the real-time surveyed medical database. Association rules are defined as implication of the form A->B where A and B are frequent itemsets in a transaction database. This new algorithm extends this definition to include association rules of forms A ->^B, ^A -> B and ^A -> ^B, which indicate negative associations between itemsets is called negative rules. Negative rules are generated from infrequent itemsets using multidimensional data model.

Keywords

Data Mining, Association Rules, Infrequent Itemsets Negative Rules.

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E. Ramaraj ¹, K. Ramesh Kumar ²

Affiliations
1 Computer Centre, Alagappa University, Karaikudi-630002, Tamilnadu, IN
2 Department of Computer Science and Engineering, Alagappa University, Karaikudi-630002, Tamilnadu, IN

Abstract

Association rule mining is to extract the interesting correlation and relation between the large volumes of databases. Association rule mining process is divided into two sub problem: The first problem is to find the frequent itemsets from the transaction and second problem is to construct the rule from the mined frequent itemset. Frequent itemsets generation is the prerequisite and most time overwhelming process for association rule mining. Apriori algorithm is the familiar and fundamental algorithm to generate the frequent itemsets from the transaction sets. Till now, Lot of researcher modified the Apriori in various manner like partition approach, Hash function and etc. But most efficient Apriori-like algorithms rely heavily on the minimum support constraints to prune the vast amount of non-candidate itemsets. These algorithms store many unwanted itemsets and transactions. In this paper propose a novel frequent itemsets generation algorithm. The drawback of the HEA, AprioriTId and Apriori overcome by the proposed algorithm. The proposed algorithm is an improved version of High Efficient AprioriTid (HEA) algorithm. The proposed algorithm is using the two theorems which are proposed in this paper. The proposed algorithm is tested with the synthetic retail dataset. It performed well at low supports. The experimental reports also show that proposed algorithm on an outset is faster than HEA, AprioriTID and Apriori.

Keywords

Data Mining, Association Rule Mining, Frequent Itemsets, Transaction Reduction.

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R. Sridevi ¹, E. Ramaraj ²

Affiliations
1 Manonmaniam Sundaranar University, Tirunelveli, IN
2 Department of Computer Science and Engineering, Alagappa University, Karaikudi, IN

Abstract

Association rule is a method for discovering interesting relationships between the items in large databases. For analyzing the students’ behaviour, the systems accumulate a large volume of valuable information. Since the student database includes more number of attributes it is difficult for processing. The goal of the Multidimensional Quantitative Rule Generation is to generate association rules that satisfy the minimum confidence threshold. But in some cases measuring confidence alone is not sufficient for decision making. Therefore, the Confidence measure for continuous data can be derived that agrees with the standard confidence measure while applying to binary data also. Besides we have taken one more add-on factor `Lift Ratio' which is to validate the generated Association rules that are strong enough to infer useful information. This proposed approach aims to put together the above points to generate an efficient algorithm to offer useful rules in an effective manner.

Keywords

Data Mining, Association Rules, Multidimensional, Confidence, Lift Ratio.

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P. Radha ¹, E. Ramaraj ², S. Purushothaman ³

Affiliations
1 Department of Computer Science and Engineering, Alagappa University, Karaikudi, IN
2 Computer Center, Alagappa University, Karaikudi, IN
3 Udaya School of Engineering, 629204, IN

Abstract

This paper discusses the preprocessing and classification of depression data using back propagation algorithm (BPA). In general, input vectors will not be orthogonal to each other. The proposed method of preprocessing the input vector makes possible BPA learn the input vectors. The classification performance of BPA have been shown for a minimum 80%.

Keywords

Depression Data, Back Propagation Algorithm, Polynomial Discriminant Vector (PDV).

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N. Sivranjani ¹, E. Ramaraj ²

Affiliations
1 Department of Computer Application, Alagappa University, Tamil Nadu, IN
2 Department of Computer Science, Alagappa University, Tamil Nadu, IN

Abstract

Cloud computing is the since a long time back imagined vision of enlisting as an utility, where clients can remotely store their information into the cloud to welcome the on-request stunning applications and associations from a common pool of configurable planning assets.By information outsourcing, clients can be calmed from the weight of near to information gathering and support. In this way, empowering open Auditability for cloud information putting away security is of basic criticalness so clients can swing to an outside overview social event to check the uprightness of outsourced information when required. To influence the overall public checking on course of action of data amassing security in Cloud Computing and give an assurance sparing looking at tradition. The arrangement reinforces an external analyst to audit customer’s outsourced data in the cloud without learning on the data content. This contrive fulfills bundle assessing where various assigned assessing endeavors from different customers can be played out at the same time by the TPA. In this paper, framework is watermarking strategy for Privacy Preserving Public Auditing for cloud data amassing security. To use the general population key based Homomorphism authenticator and interestingly coordinate it with arbitrary veil procedure to accomplish a protection saving open inspecting framework for cloud information stockpiling security while remembering every single above prerequisite. The fundamental plan to help cluster evaluating for TPA upon designations from multi-clients. It utilizes Merle Hash Tree (MHT) for it. To presenting Privacy Preserving Public examining with watermark process for secure distributed storage.

Keywords

Cloud Storage, Privacy-Preserving, TPA, Public Auditing.

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