A search space reduction methodology for data mining in large databases

Kuri-Morales, Ángel; Rodríguez-Erazo, Fátima

doi:10.1016/j.engappai.2008.04.003

Cited by 39 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the literature, many related studies have shown promising results for feature selection and instance selection approaches [25,35,42,50,53]. However, up until now, the focus has been on either selecting more representative features or reducing faulty data, as it relates to effective classification or prediction.…”

Section: Introductionmentioning

confidence: 98%

Genetic algorithms in feature and instance selection

Tsai

Eberle

Chu

2013

Knowledge-Based Systems

215

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 98%

Genetic algorithms in feature and instance selection

Tsai

Eberle

Chu

2013

Knowledge-Based Systems

215

View full text Add to dashboard Cite

“…[17] More details regarding the K-MICA clustering technique can be found in. [17][18][19][20][21][22][23][24][25][26] Then the Euclidean distance of each pattern (benign and malignant) is computed from the determined clusters. It is used as the characteristic feature.…”

Section: General Structure Of the Proposed Methodsmentioning

confidence: 99%

A research about breast cancer detection using different neural networks and K-MICA algorithm

et al. 2013

View full text Add to dashboard Cite

Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and the simulation results show that the recommended system has high accuracy.

show abstract

“…Another one is that it converges to local minimum. Recently, numerous ideas have been used to alleviate this drawback by using global optimization algorithms such as GA (Krishna & Murty 1999), TS (Ng & Wong 2002), PSO and hybrid K-PSO (Kao et al 2008), hybrid PSO-SA (Niknam et al 2008a(Niknam et al , 2008b, hybrid PSO-ACO-K (Bahmani Firouzi et al 2010), HBMO (Fathian et al 2007), hybrid ACO-SA (Niknam et al 2008a(Niknam et al , 2008b and PSO-SA-K (Morales & Erazo 2009). The aim of this paper is using the combination method to solve these problems.…”

Section: Hybrid K-micamentioning

confidence: 98%

A new hybrid imperialist competitive algorithm on data clustering

Niknam

Fard

Ehrampoosh

et al. 2011

Sadhana

View full text Add to dashboard Cite

Clustering is a process for partitioning datasets. This technique is very useful for optimum solution. k-means is one of the simplest and the most famous methods that is based on square error criterion. This algorithm depends on initial states and converges to local optima. Some recent researches show that k-means algorithm has been successfully applied to combinatorial optimization problems for clustering. In this paper, we purpose a novel algorithm that is based on combining two algorithms of clustering; k-means and Modify Imperialist Competitive Algorithm. It is named hybrid K-MICA. In addition, we use a method called modified expectation maximization (EM) to determine number of clusters. The experimented results show that the new method carries out better results than the ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and k-means.

show abstract

A search space reduction methodology for data mining in large databases

Cited by 39 publications

References 14 publications

Genetic algorithms in feature and instance selection

Genetic algorithms in feature and instance selection

A research about breast cancer detection using different neural networks and K-MICA algorithm

A new hybrid imperialist competitive algorithm on data clustering

Contact Info

Product

Resources

About