An Attributes Similarity-Based K-Medoids Clustering Technique in Data Mining

Narayana, G. Surya; Vasumathi, D.

doi:10.1007/s13369-017-2761-2

Cited by 18 publications

(7 citation statements)

References 24 publications

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“…Practical data is contaminated by faults (errors) and missing values. Preprocessing assigns missing values, treats noise, normalizes, transforms, integrates, mitigates inconsistencies, and reduces and discretizes data [37]. Geospatial clustering datasets often include missing values.…”

Section: Proposed Methodsmentioning

confidence: 99%

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

et al. 2022

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The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an optimized hierarchical distributed k-medoid symmetric clustering algorithm for big data spatial query processing. To increase the k-medoid method’s efficiency and create more precise clusters, a hybrid approach combining the k-medoid and Chemical Reaction Optimization (CRO) techniques is presented. CRO is used in this approach to broaden the scope of the optimal medoid and improve clustering by obtaining more accurate data. The suggested paradigm solves the current technique’s issue of predicting the accurate clusters’ number. The suggested approach includes two phases: in the first phase, the local clusters are built using Apache Spark’s parallelism paradigm based on their portion of the whole dataset. In the second phase, the local clusters are merged to create condensed and reliable final clusters. The suggested approach condenses the data provided during aggregation and creates the ideal clusters’ number automatically based on the dataset’s structures. The suggested approach is robust and delivers high-quality results for spatial query analysis, as shown by experimental results. The proposed model reduces average query latency by 23%.

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Section: Proposed Methodsmentioning

confidence: 99%

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

et al. 2022

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“…The new medoid is determined by multiplying the intra-cluster distance matrix by rows and choosing the element with the smallest sum of pairwise distances. Algorithm 1 illustrates the local clustering process [21] [27]. Both local clustering algorithms are performed in parallel without coordination between nodes in this implementation.…”

Section: Step 3: First Stage Clustering a Local Clustering Using Entr...mentioning

confidence: 99%

“…Data reduction methods may be used to create a compressed version of the dataset that is significantly smaller in volume but retains the initial data's consistency [27][28][29][30][31][32][33]. Numerous data reduction techniques are described in the literature; these include sampling, data compression, and data discretization.…”

Section: B Context-aware Reductionmentioning

confidence: 99%

Clustering Big Data Based on Distributed Fuzzy K-Medoids: An Application to Geospatial Informatics

2022

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The advent of big data related to spatial position knowledge, called geospatial big data, provides us with opportunities to recognize the urban environment. Existing database processing methods are inadequate to rapidly provide reliable results in a geospatial big data context due to the need for defining approximation "measures" and the increasing execution time for the queries. The clustering method yields the functional effects. However, how to scale and accelerate clustering algorithms while maintaining high clustering efficiency remains a significant challenge. The paper's primary contribution is the introduction of a modified hierarchical distributed k-medoid clustering method that is specific to spatial query analysis for big data. To improve the efficiency of the k-medoid algorithm and obtain more precise clusters, the suggested model utilizes Fuzzy k-Medoids method to overcome outlier appearance on the spatial data set and to deal with data uncertainty. The method is complex in nature since it is not predicated on the number of right clusters. The proposed model is divided into two phases: the first step creates local clusters based on its portion of the entire dataset; this stage makes extensive use of the parallelism paradigm provided by the Apache Spark framework; and the second phase aggregates the local clusters to produce compact and reliable final clusters. The proposed model greatly decreases the amount of knowledge shared during the aggregation process and automatically produces the appropriate number of clusters based on the dataset characteristics. The results show that the proposed model outperforms the traditional K-medoids in terms of accuracy of obtained centers in big data applications.

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“…Second, some scholars introduce the Swarm Intelligence [ 30 , 31 ] and combine it with K-medoids to improve the global search capability and efficiency of the improved algorithms for samples. Arthur and Vassilvitskii [ 32 ] algorithmically fused the Swarm Algorithm with K-medoids.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm

Jin

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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In view of the shortcomings of traditional clustering algorithms in feature selection and clustering effect, an improved Recency, Frequency, and Money (RFM) model is introduced, and an improved K-medoids algorithm is proposed. Above model and algorithm are employed to segment customers of e-commerce. First, traditional RFM model is improved by adding two features of customer consumption behavior. Second, in order to overcome the defect of setting K value artificially in traditional K-medoids algorithm, the Calinski–Harabasz (CH) index is introduced to determine the optimal number of clustering. Meanwhile, K-medoids algorithm is optimized by changing the selection of centroids to avoid the influence of noise and isolated points. Finally, empirical research is done using a dataset from an e-commerce platform. The results show that our improved K-medoids algorithm can improve the efficiency and accuracy of e-commerce customer segmentation.

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An Attributes Similarity-Based K-Medoids Clustering Technique in Data Mining

Cited by 18 publications

References 24 publications

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

Clustering Big Data Based on Distributed Fuzzy K-Medoids: An Application to Geospatial Informatics

Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm

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