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Chidambaram, M.
- Frequent Pattern Technique using Federation Rule Mining
Abstract Views :145 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 Computer Science Department, Rajah Serfoji Government College, Thanjavur, Tamil Nadu, IN
1 Department of Computer Science, Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 Computer Science Department, Rajah Serfoji Government College, Thanjavur, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 38 (2016), Pagination:Abstract
Objectives: To improve the security, as well as privacy while sharing data/information to third parties. From the database Duplicate data were eliminated and extracted the original database Methods/Statistical Analysis: FP tree based algorithm was proposed in this paper. It is used to generate the frequent item data sets. Those frequent data Item sets are extracted by using inverse data item set. It must achieve good security and privacy. Findings: The main problem in existing system is information leakage. In frequent pattern technique, federation rule mining process which tries to find some correlations and associations among the various types of data items in a dataset. It finds more privacy preserving techniques related to the data mining process. Applications/Improvement: To compare and evaluate the proposed of many algorithms, federation rule mining should able to maintain the data privacy in a proper manner.Keywords
Federation Rule Mining, Frequent Pattern, Crypt Analysis, Data Contortion, Data Sensation.Full Text
- Privacy Preserving Association Rule Mining in Distributed Environments using Fp-Growth Algorithm and Elliptic Curve Cryptography
Abstract Views :165 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 Computer Science Department, Rajah Serfoji Government College, Thanjavur, Tamil Nadu, IN
1 Department of Computer Science, Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 Computer Science Department, Rajah Serfoji Government College, Thanjavur, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 48 (2016), Pagination:Abstract
In this paper, we present a privacy preserving association rule mining method in distributed environments. The proposed method uses FP-growth algorithm and combination of elliptic curve cryptography and digital signature. The proposed method is low in cost for both computation and communication. This method uses associative third party, which is the central authority, holding common cryptographic keys of all the sites and responsible for performing distributed ARM. This method maintains privacy regardless of any number of dishonest sites in the distributed environment.Keywords
Association Rule Mining, ARM, Cryptography, Digital Signature, Elliptic Curve, FP-Growth, Privacy in ARMFull Text
- A Novel Hybrid Genetic Algorithm with Weighted Crossover and Modified Particle Swarm Optimization
Abstract Views :226 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore-641046, IN
2 Department of Computer Science, Rejah Serfoji Government College, Thanjavur-613005, IN
1 Department of Computer Science, Bharathiar University, Coimbatore-641046, IN
2 Department of Computer Science, Rejah Serfoji Government College, Thanjavur-613005, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 2 (2017), Pagination: 25-30Abstract
The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although these methods are approximate methods (i.e. their solutions are good, but probably not optimal), they do not require the derivatives of the objective function and constraints. Also, the heuristics use probabilistic transition rules instead of deterministic rules. Here, an evolutionary algorithm based on the hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), denoted by HGAPSO, is developed. Particle Swarm Optimization (PSO) is a very popular optimization technique, but it suffers from a major drawback of a possible premature convergence i.e. convergence to a local optimum and not to the global optimum. This paper attempts to improve on the reliability of PSO by addressing the drawback. This modified method would free PSO from local optimum solutions; enable it to progress towards the global optimum searching over wider area. So the probability, of not getting trapped into local optima gets enhanced which gives better assurance to the achieved solution. Experiments shows that the proposed method will provide better solution.
Keywords
Particle Swarm Optimization, Genetic Algorithm, Hybrid Algorithm, Modified Particle Swarm.
- An Efficient Hybrid of Continuous Ant Colony Optimization and Weighted Crossover Genetic Algorithm for Optimal Solution
Abstract Views :209 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Rejah Serfoji Government College, Thanjavur, IN
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Rejah Serfoji Government College, Thanjavur, IN
Source
Fuzzy Systems, Vol 10, No 1 (2018), Pagination: 1-7Abstract
In real time applications the optimization problems that are hard to solve. To solve these kind of problems the algorithms should be specialized and applicable for large range of problems, or they are more general but rather inefficient. In which Evolutionary Algorithms (EA) are more popular which consist of several search heuristics by imitating some features of natural evolution and the social behavior of species. This heuristics algorithm are developed to solve optimization problem but it effectively fail because of convergence and computation time. To overcome this flaws a novel hybrid evolutionary algorithm as Genetic Algorithm (GA) - Continuous Ant Colony Optimization (CACO) is developed. The weighed crossover operation is introduced in genetic algorithm to select the crossover operator. CACO is utilized as a GA mutation then the GA output is given as an input to the CACO. Then the genetic algorithm undergoes the selection, crossover and it gives the result. Based on the comparative analysis, the performance results show the better efficiency and capabilities in finding the optimum solutions.Keywords
Evolutionary Algorithms, Optimization, Weighted Crossover, Genetic Algorithm (GA) and Ant Colony Optimization (ACO).References
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