[vc_empty_space][vc_empty_space]
Comparison of distance and dissimilarity measures for clustering data with mix attribute types
Prasetyo H.a, Purwarianti A.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, West-Java, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2014 IEEE.Clustering is one of the most popular methods in data mining. Many algorithms can be applied for data clustering with numeric or categorical attributes. However, most of data in the real world contain both numeric and categorical attributes. A clustering method which can be applied on attributes in mix types become important to handle the problem. K-prototypes algorithm is one of the algorithms which can deal for clustering data with mix attribute types. However, it has a drawback on its dissimilarity measure between categorical data. The selection of proper dissimilarity measure between categorical data is thus important to increase its performance. This paper compares distance and dissimilarity measures for clustering data with mix attribute types. We used the k-prototypes algorithm on UCI datasets, i.e. Echocardiogram, Hepatitis, and Zoo, to assign cluster membership of the objects. Silhouette index was employed to evaluate clustering results. The results show that Euclidean distance and Ratio on Mismatches dissimilarity are the best combination for clustering data with numeric and categorical attribute types, as it shown with average Silhouette index towards 1. As a result, to cluster data with mix attribute types, we propose to employ Euclidean distance and Ratio on Mismatches dissimilarity to be applied on k-prototypes algorithm.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Categorical attributes,Cluster memberships,Clustering methods,clustering mix types data,Clustering results,Dissimilarity measures,Euclidean distance,K-prototype[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]clustering mix types data,data mining,distance and dissimilarity measures,k-prototypes algorithm[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICITACEE.2014.7065756[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]