Automatic clustering by multi-objective genetic algorithm with numeric and categorical features

Dutta, Dipankar; Sil, Jaya; Dutta, Paramartha

doi:10.1016/j.eswa.2019.06.056

Cited by 44 publications

(28 citation statements)

References 79 publications

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“…In order to avoid the curse of the dimensionality and improve the classification accuracy of the model as well, feature selection is considered as an effective method to pick up most representative subset of features from the data set. In this paper, GA is employed to realize the feature selection method because it has powerful searching ability [30] and been successfully applied in feature selection [31,32]. us, GA is developed to construct an efficient ECOC ensemble system with feature selection.…”

Section: Ga-based Feature Selection Methodsmentioning

confidence: 99%

Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

Xie

Chen

Liu

et al. 2021

Scientific Programming

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In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.

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Section: Ga-based Feature Selection Methodsmentioning

confidence: 99%

Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

Xie

Chen

Liu

et al. 2021

Scientific Programming

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“…Some of the genetic algorithm based clustering researches are discussed in this section. In this paper K-clustering algorithm is combined with genetic algorithms to develop a multi-objective genetic algorithm (MOGA) to improve the performance of the network [ 40 ]. To improve the network longevity clustering model is constructed in heterogeneous WSN (HWSN) network as well as genetic algorithm-based optimized clustering (GAOC) protocol is introduced with multiple data sinks model called MS-GAOC.…”

Section: Related Workmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

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“…In this section, a discussion of various existing techniques is presented, which are used to cluster the categorical data. The advantages and its limitations of these existing techniques [14][15][16][17][18] are also illustrated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Dutta [18] discovered an optimal value of K by designing an automatic clustering algorithm. The iterative hill-climbing algorithms namely Genetic Algorithm (GA) and K-Means were used to identify the local and global optimum solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Similarity based K-Means Clustering Technique for Categorical Data in Data Mining Application

Kumar¹,

Kanavalli²

2021

IJIES

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Clustering plays a major role in the data mining application, because it divides and groups the data effectively. In the pattern analysis, two major challenges occur in real-life applications that includes handling the categorical data and the availability of correctly labeled data. According to the characteristics of homogeneity, the clustering techniques are designed to group the unlabeled data. Some important issues such as high memory utilization, time consumption, overhead, computation complexity and less effective results are present in various existing algorithms of numerical data. Therefore, the research study implemented clustering techniques based on the similarity of categorical data. Simultaneously, the attributes of inter and intra-clusters' similarities are identified, and then the performance of proposed method is improved by integrating those similarities. The noises are also removed by performing the pre-processing techniques, so the similarity between noise-free elements are estimated. Once these similarities are identified, the insignificant attributes are removed and the relevant attributes are chosen from the preprocessed elements. The overhead is reduced by developing the Similarity-based K-means Clustering (SKC) approach for clustering the attributes that depends on divergence distance. The efficiency of SKC is tested in the experimental analysis by means of precision, f-measure, accuracy, error rate of clustering and recall. The results state that the developed study achieved 98.45% accuracy for the publicly available dataset when comparing with the existing techniques: variations of Particle Swarm Optimization (PSO) and semi-supervised clustering system.

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Automatic clustering by multi-objective genetic algorithm with numeric and categorical features

Cited by 44 publications

References 79 publications

Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

A Similarity based K-Means Clustering Technique for Categorical Data in Data Mining Application

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