An Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality Reduction

Suguna,

doi:10.3844/ajassp.2011.261.266

Cited by 48 publications

(23 citation statements)

References 10 publications

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“…So, it is bound with some defaults, which may affect in some particular data optimizations. In order to improve the performance, a new algorithm is proposed to replace the existing ABC algorithm (Dervis and Ozturk, 2011;Changsheng et al, 2010;Bahriye and Karaboga, 2012;Suguna, 2011;Visu et al, 2012). In this approach, an ABC algorithm with FCM operator is introduced to improve the optimization efficiency of ABC algorithm.…”

Section: Jcsmentioning

confidence: 99%

“…Clustering approaches can be divided as, partitioning methods, hierarchical methods, (Yu et al, 2010), (Krinidis and Chatzis, 2010), fuzzy clustering (Dervis and Ozturk, 2010). Suguna (2011) density based clustering, artificial neural clustering, statistical clustering, grid based, mixed and more (Cheng-Fa and Yen, 2007). Among these approaches, partitional and hierarchical clustering algorithms are the two important approaches in research areas.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

Makkithaya¹,

Natarajan²

2013

Journal of Computer Science

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Clustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorithm is that it uses a Fuzzy CMeans (FCM) operator in the Artificial Bee Colony (ABC) algorithm. The area of action of the FCM operator comes at the scout bee phase of the ABC algorithm as the scout bees are introduced by the FCM operator. The experimental results have shown that the proposed approach has provided significant results in terms of the quality of solution. The comparative study of the proposed approach with existing algorithms in the literature using the datasets from UCI Machine learning repository is satisfactory.

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Section: Jcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

Makkithaya¹,

Natarajan²

2013

Journal of Computer Science

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“…This objective function is proposed by Jensen et al [30] that selects the minimum number of selected features and maximum classification accuracy, it is shown in equation (6). The proposed FS-BCS is presented in Algorithm 1.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

Alia¹,

Taweel²

2017

IJITCS

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Abstract-Feature Selection (FS) is an important process to find the minimal subset of features from the original data by removing the redundant and irrelevant features. It aims to improve the efficiency of classification algorithms. Rough set theory (RST) is one of the effective approaches to feature selection, but it uses complete search to search for all subsets of features and dependency to evaluate these subsets. However, the complete search is expensive and may not be feasible for large data due to its high cost. Therefore, meta-heuristics algorithms, especially Nature Inspired Algorithms, have been widely used to replace the reduction part in RST. This paper develops a new algorithm for Feature Selection based on hybrid Binary Cuckoo Search and rough set theory for classification on nominal datasets. The developed algorithm is evaluated on five nominal datasets from the UCI repository, against a number of similar NIAs algorithms. The results show that our algorithm achieves better FS compared to two known NIAs in a lesser number of iterations, without significantly reducing the classification accuracy.

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“…In recent times, numerous data mining applications (Syurahbil et al, 2009) and models have been created for diverse fields, like marketing, banking, finance, production, health care and other kinds of scientific data (Suguna and Thanushkodi, 2011;(Sarlak and Fard, 2009). Parameters that create defects in manufacturing processes can be identified using data mining methods utilized in other complicated fields like Customer Relationship Management (CRM) (Batmaz et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Technique to Find Optimal Customers for Beneficial Customer Relationship Management

Babu¹

2012

Journal of Computer Science

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Problem statement: Modern companies and organizations are efficiently implementing a CRM strategy for managing a company interactions and relationships with customers. CRM systems have been developed and designed to support the areas of marketing, service process and sales. Many literature studies are available to preserve the customer relationship but small drawbacks occur in the existing methods. One method to maintain the customer relationship is frequency based method i.e., the company will give declination to the customer based on the historical data that is the customers how many times come to that company. These methods are not effective. Because the customers give revenue to that company is less. So the company revenue is affected. Approach: In this study, we propose a data mining and artificial technique to maintain the customer relationship between company and customers. Accomplishing this process, we maintain a historical database and then we use data mining ARM technique to mine the customer information from this database. We then present an artificial intelligence PSO technique to provide an offer to the selected customers. This offer does not affect the company revenues as well as satisfies the customers. This process will make a best relationship between the customers and organization and to satisfy the customers forever with company's rules. Results: he simulation results have shown the performance of apriori algorithm for diverse combination of profit lengths of each customer. The performance of selected customer has been analyzed for five years. Finally the comparison results shows the selected customers are optimal comparing to other customers. Conclusion: The proposed method optimally selects the customers and also avoids the problems in the earliest methods. So, the relationship between customer and company is maintained successfully by using the proposed method.

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An Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality Reduction

Cited by 48 publications

References 10 publications

Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

A Data Mining Technique to Find Optimal Customers for Beneficial Customer Relationship Management

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