The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set
Abstract
In recent days, researchers are doing research studies for clustering of data which are heterogeneous in nature. The data generated in many real-world applications like data form IoT environments and big data domains are heterogeneous in nature. Most of the available clustering algorithms deal with data in homogeneous nature, and there are few algorithms discussed in the literature to deal the data with numeric and categorical nature. Applying the clustering algorithm used by homogenous data to the heterogeneous data leads to information loss. This chapter proposes a new genetically-modified k-medoid clustering algorithm (GMODKMD) which takes fused distance matrix as input that adopts from applying individual distance measures for each attribute based on its characteristics. The GMODKMD is a modified algorithm where Davies Boudlin index is applied in the iteration phase. The proposed algorithm is compared with existing techniques based on accuracy. The experimental result shows that the modified algorithm with fused distance matrix outperforms the existing clustering technique.
Related Content
|
Vinod Kumar, Himanshu Prajapati, Sasikala Ponnusamy.
© 2023.
18 pages.
|
|
Sougatamoy Biswas.
© 2023.
14 pages.
|
|
Ganga Devi S. V. S..
© 2023.
10 pages.
|
|
Gotam Singh Lalotra, Ashok Sharma, Barun Kumar Bhatti, Suresh Singh.
© 2023.
15 pages.
|
|
Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma.
© 2023.
16 pages.
|
|
R. Soujanya, Ravi Mohan Sharma, Manish Manish Maheshwari, Divya Prakash Shrivastava.
© 2023.
12 pages.
|
|
Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma.
© 2023.
22 pages.
|
|
|