Clustering voluminous of heterogeneous data

Kumar, M. Pramod; Rani, K. Swarupa; Rao, C. Raghvendra

doi:10.1109/iccci.2017.8117706

Cited by 2 publications

(1 citation statement)

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“…The drawback with the proposed approach is that it is the same as the Huang's approach, and the only difference being the inclusion of map-reduce algorithm in it to handle big data. In a recent paper by Kumar, Rani and Rao [10] which had suggested a feature scaling approach for handling mixed data using the k-prototypes algorithm. Their idea was to normalise the range of the numerical data.…”

Section: Partition Based Clustering Algorithm For Mixed Datamentioning

confidence: 99%

A Partition-Based Feature Selection Method for Mixed Data: A Filter Approach

Dutt

Ismail

2020

MJCS

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Feature selection is fundamentally an optimization problem for selecting relevant features from several alternatives in clustering problems. Though several algorithms have been suggested, however till this day, there has not been any one of those that has been dubbed as the best for every problem scenario. Therefore, researchers continue to strive in developing superior algorithms. Even though clustering process is considered a pre-processing task but what it really does is just dividing the data into groups. In this paper we have attempted an improved distance function to cluster mixed data. A similarity measure for mixed data is Gower distance is adopted and modified to define the similarity between object pairs. A partitional algorithm for mixed data is employed to group similar objects in clusters. The performance of the proposed method has been evaluated on similar mixed and real educational dataset in terms of the silhouette coefficient. Results reveal the effectiveness of this algorithm in unsupervised discovery problems. The proposed algorithm performed better than other clustering algorithms for various datasets.

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Section: Partition Based Clustering Algorithm For Mixed Datamentioning

confidence: 99%