Data Mining and Knowledge Engineering

M. H. Marghny ¹, Ahmed I. Taloba ¹, Rasha M. Abd El-Aziz ¹

Affiliations
1 Department of Computer Science, Assiut University, EG

Abstract

Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration when one want to solve clustering problem of balanced data. Mostly, numerical experiments show that FBK-means is faster and more accurate than the K-means algorithm, Genetic Algorithm, and Bee algorithm. FBK-means Algorithm needs few distance calculations and fewer computational time while keeping the same clustering results. However, the FBK-means algorithm doesn't give good results with imbalanced data. To resolve this shortage, a more efficient clustering algorithm, namely Fast K-means (FK-means), developed in this paper. This algorithm not only give the best results as in the FBK-means approach but also needs lower computational time in case of imbalance data.

Keywords

Clustering, K-Means Algorithm, Bee Algorithm, Genetic Algorithm, FBK-Means Algorithm, FK-Means Algorithm.

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M. H. Marghny ¹, Rasha M. Abd El-Aziz ¹

Affiliations
1 Computer Science Department, Assiut University, EG

Abstract

Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) is an extremely fast and simple algorithm for generating linear and nonlinear classifiers. Kernel functions are essential in fitting GEPSVM. Usually a single kernel is used by most researchers in their studies, but the real world applications may require a combination of multiple kernel functions. There are two kind of kernels which known as global and local kernels. Global kernel functions have good generalization ability, but low learning ability. Local kernel functions have good learning ability with weak generalization. The presented approach constructs a mixed kernel function with better performance by fully combining local kernel function for strong learning ability and global kernel function for strong generalization. The Backtracking Search Algorithm (BSA) is used for determining the best value of the weight parameter between the two kernels. To evaluate the performance of the proposed approach, we applied it to public datasets from UCI repository.

Keywords

Support Vector Machine, Generalized Eigenvalues, Kernel Functions, Backtracking Search Algorithm.

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