Clustering Mixed Numeric and Categorical Data With Cuckoo Search

Abstract

Clustering analysis, as an important technique in data mining, aims to identify the nature groups or clusters of data objects in the attribute space. Data objects in real-world applications are commonly described by both numeric and categorical attributes. In this research, considering that the partitional clustering algorithms designed for this type of mixed data are prone to get trapped into local optima and the cuckoo search approach is efficient in solving global optimization problems, we propose CCS-K-Prototypes, a novel partitional Clustering algorithm based on Cuckoo Search and K-Prototypes, for clustering mixed numeric and categorical data. To deal with different types of attributes, we develop a novel representation for candidate solutions, and suggest two formulas for the cuckoo to search for the potential solution around the existing solutions or in the entire attribute space. Finally, the performance of the proposed algorithm is assessed by a series of experiments on five benchmark datasets. INDEX TERMS Data clustering, cuckoo search, mixed data, numeric and categorical attributes.

The Initialization of Flexible K-Medoids Partitioning Method Using a Combination of Deviation and Sum of Variable Values

“…Figure 5 illustrates the comparison between the adjusted Rand index of the new method and others [7,8,12,[24][25][26]. Although it is unfair to compare with other methods because the distances used may vary, from the graph of the ARI the proposed method outperforms others.…”

“…Table 12 depicts the detail of adjusted Rand index values and clustering accuracy from the proposed methods and several others [7,8,12,25,26]. The SFKM method was applied for ionosphere and heart disease case 1, with the same data used to obtain ARI and clustering accuracy.…”

The Categorical Data Conundrum: Heuristics for Classification Problems—A Case Study on Domestic Fire Injuries

“…In contrast, categorical variables tend to hide (even mask) a great deal of the interesting information in a dataset [4,5,6]. It is not so easy to see trends and make predictions or forecasts when categorical variables dominate the dataset [7,8]. This makes it crucial to develop systematic methods and heuristics for dealing with such variables.…”

MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

“…Moreover, explore the other swarm intelligence algorithms for clustering categorical data as well as mixed. Ji, Pang, Li, He, Feng, & Zhao (2020) proposed the Novel partitional Clustering algorithm based on Cuckoo Search and K-Prototypes (CCS-K-Prototypes) for clustering mixed numeric and categorical data. It finds the global solutions for the different types of attributes.They also suggested the multi-objective optimisation approaches to clustering mixed data and multi-view clustering and deep clustering for mixed data.…”