Yugal Kumar

A charged system search approach for data clustering

This paper presents a charged system search optimization method for finding the optimal cluster centers in a given dataset. CSS algorithm utilizes the Coulomb and Gauss laws from electrostatics to initiate the local search, and Newton second law of motion from mechanics is employed for global search. The efficiency and capability of the proposed algorithm are evaluated on seven datasets and compared with existing

A two-step artificial bee colony algorithm for clustering

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Modified Teacher Learning Based Optimization Method for Data Clustering

K-means, GA, PSO and ACO algorithms. From the experimental results, it is found that the proposed algorithm provides more accurate and effective results than other methods being compared.

A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm

The prediction of Parkinsons disease in early age has been challenging task among researchers because the symptoms of disease come into existence in middle and late middle age. There is lot of the symptoms that leads to Parkinsons disease. But this paper focus on the speech articulation difficulty symptoms of PD affected people and try to formulate the model on the behalf of three data mining methods. These three data mining methods are taken from three different domains of data mining i.e. from tree classifier, statistical classifier and support vector machine classifier. Performance of these three classifiers is measured with three performance matrices i.e. accuracy, sensitivity and specificity. So, the main task of this paper is tried to find out which model identified the PD affected people more accurately.

Seminal quality prediction using data mining methods

Abstract In the field of data analysis, clustering is a powerful technique which groups the data into different subsets using a distance function. Data belonging to the same subset are similar in nature and offer heterogeneity to the data that reside in other subsets. Clustering has proved its potentiality in various fields such as bioinformatics, pattern recognition, image processing and many more. In this paper, a two-step artificial bee colony (ABC) algorithm is proposed for efficient data clustering. In two-step ABC algorithm, the initial positions of food sources are identified using the K-means algorithm instead of random initialization. Along this, to discover the promising search areas, an improved solution search equation based on social behavior of PSO is applied in the onlooker bee phase of ABC algorithm and abandoned food source location is found by using Hooke and Jeeves-based direct search method. Five benchmark and two artificial datasets are applied to validate the proposed modifications in the ABC algorithm, and results of this study are compared with other well-known clustering algorithms. Both the experimental and statistical analyses show that improvements in ABC algorithm have an advantage over the conventional ABC algorithm for solving clustering problems.

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

BACKGROUND Diagnosing different types of liver diseases clinically is a quite hectic process because patients have to undergo large numbers of independent laboratory tests. On the basis of results and analysis of laboratory test, different liver diseases are classified. Hence to simplify this complex process, we have developed a Rule Base Classification Model (RBCM) to predict different types of liver diseases. The proposed model is the combination of rules and different data mining techniques. OBJECTIVE The objective of this paper is to propose a rule based classification model with machine learning techniques for the prediction of different types of Liver diseases. METHOD A dataset was developed with twelve attributes that include the records of 583 patients in which 441 patients were male and rests were female. Support Vector Machine (SVM), Rule Induction (RI), Decision Tree (DT), Naive Bayes (NB) and Artificial Neural Network (ANN) data mining techniques with K-cross fold technique are used with the proposed model for the prediction of liver diseases. The performance of these data mining techniques are evaluated with accuracy, sensitivity, specificity and kappa parameters as well as statistical techniques (ANOVA and Chi square test) are used to analyze the liver disease dataset and independence of attributes. RESULT Out of 583 patients, 416 patients are liver diseases affected and rests of 167 patients are healthy. The proposed model with decision tree (DT) technique provides the better result among all techniques (RI, SVM, ANN and NB) with all parameters (Accuracy 98.46%, Sensitivity 95.7%, Specificity 95.28% and Kappa 0.983) while the SVM exhibits poor performance (Accuracy 82.33%, Sensitivity 68.03%, Specificity 91.28% and Kappa 0.801). It is also found that the best performance of the model without rules (RI, Accuracy 82.68%, Sensitivity 86.34%, Specificity 90.51% and Kappa 0.619) is almost similar to the worst performance of the rule based classification model (SVM, Accuracy 82.33%, Sensitivity 68.03%, Specificity 91.28% and Kappa 0.801 and the accuracy of chi square test is 76.67%. CONCLUSION This study demonstrates that there is a significant difference between the proposed rules based classification model and the model without rules for the liver diseases prediction and the rule based classification model with decision tree (DT) technique provides most accurate result. This model can be used as a valuable tool for medical decision making.

Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy

Clustering is an important task in engineering domain which can be applied in many applications. Clustering is a process to group the data items in the form of clusters such that data items in one cluster have more similarity to other clusters. On the other side, Teaching–Learning Based Optimization (TLBO) algorithm is a latest population based optimization technique that has been effectively applied to solve mechanical design problems and also utilized to solve clustering problem. This algorithm is based on unique ability of teacher i.e. how the teacher influence the learners through its teaching skills. This algorithm has shown good potential to solve clustering problems but it is still suffering with some problems. In this paper, two modifications have been proposed for TLBO method to enhance its performance in clustering domain instead of random initialization a predefined method previously used to exploit initial cluster centers as well as to deal the data vectors that cross the boundary condition. The performance of proposed modified TLBO algorithm is evaluated with six dataset using quantization error, intra cluster distance and inters cluster distance parameters and compared with K-Means, Particle Swarm Optimization (PSO) and TLBO. From the experimental results, it is clearly obvious that the proposed modifications have shown better results as compared to previously ones.

An Improved Cat Swarm Optimization Algorithm for Clustering

Clustering is an important task that is used to find subsets of similar objects from a set of objects such that the objects in the same subsets are more similar than other subsets. Large number of algorithms has been developed to solve the clustering problem. K-Harmonic Mean (KHM) is one of the popular technique that has been applied in clustering as a substitute of K-Means algorithm because it is insensitive to initialization issues due to built in boosting function. But, this method is also trapped in local optima. On the other hand, Cat Swarm Optimization (CSO) is the latest population based optimization method used for global optimization. In this paper a hybrid data clustering method is proposed based on CSO and KHM which includes the advantage of both algorithms and named as CSOKHM. The hybrid CSOKHM not only improved the convergence speed of CSO but also escape the KHM method to run in local optima. The performance of the CSOKHM is evaluated using seven datasets and compared with KHM, PSO, PSOKHM, ACA, ACAKHM, GSAKHM, CSO methods. The experimental results show the applicability of CSOKHM method..