Parallel CLARANS Algorithm for Recommendation System in Multi-cloud Environment

Indira, K.; Karthiga, S.; Angeline, C. V. Nisha; Santhiya, C.

doi:10.1007/978-981-15-9647-6_36

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…A number of clustering algorithms targeting a variety of scenarios has been developed and studied [5]- [9]. Some existing clustering algorithms are able to produce a pre-defined number K of good quality clusters from high-dimensional large data that do not include outliers or noise, e.g., K-means [10], K-medioids [11], PAM [12], and CLARANS [13]. Other algorithms, including GMM [14], and Self-Organizing Maps (SOM) [15] are not sensitive to outliers or noise, and can produce good-quality clusters from high-dimensional large data.…”

Section: Introductionmentioning

confidence: 99%

Piecemeal Clustering: a Self-Driven Data Clustering Algorithm

et al. 2022

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Various approaches have been discussed in the literature for the clustering of data, such as partitioning, hierarchical, and machine learning methods. Most of the approaches require some prior knowledge about the clusters, such as their total number. Furthermore, some previous algorithms are not robust enough to process higher-dimensional data or require a large amount of memory for computations. We propose, herein, a data clustering algorithm, Piecemeal Clustering, that successfully clusters data without prior knowledge of the number of clusters. The proposed clustering algorithm uses the similarity and density of the data to identify the number of clusters in the data set and works with both low-and high-dimensional data. We demonstrate the power of the proposed Piecemeal Clustering algorithm with two real-world data sets. It is found that the proposed algorithm outperforms seven other state-of-the-art algorithms on both of these data sets.

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