A Hybrid Intelligent Clustering Model for Tackling Incomplete Mixed Data Using Heuristic Algorithm with Artificial Intelligence

Abstract

Recently, in various information areas including image processing and social networks, data clustering has been vastly utilized. Thus, the clustering task is taken as successful while imputing the quality of uncertain values, whereas the traditional methods observe only poor performance. To enhance the performance, the hybrid intelligent-based clustering model is proposed by the adaptive concept. Initially, the data is collected from benchmark datasets that are composed of incomplete mixed data. The first level of clustering is processed by optimized K-Means Clustering (KMC) to acquire the optimal centroid, in which the centroid is optimized by the Adaptive Probability-assisted Water Strider Algorithm (AP-WSA). Similarly, the second clustering is done through K-Correlation-based Clustering (KCC) to obtain the clustered output. Thus, the final clustering process is named as Hybrid K-Means K-Correlation (HKMKC) clustering algorithm. Finally, the experimental results are validated with the performance measures and estimated with the traditional methods. Through the result analysis, the Average Silhouette Coefficient (AS) of the designed AP-WSA-HKMKC is 50%, 28%, 16%, and 5.45% progressed than KMC, FCM, KCC, and K-median clustering methods for dataset 2 at 15% miss rate. Thus, the designed hybrid clustering ensures the efficiency of handling incomplete data regarding accurate clustered outputs.

Keywords:

• Adaptive Probability-assisted Water Strider Algorithm
• artificial intelligence
• hybrid intelligent clustering model
• Hybrid K-Means K-Correlation
• K-Correlation based clustering
• K-Means clustering
• processing incomplete mixed data

Data Availability Statement

The data underlying this article are available in the iris dataset, at “https://www.kaggle.com/datasets/uciml/iris?select=database.sqlite: Access Date: 2022-06-27.”

The data underlying this article are available in the red wine quality dataset, at “https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009: Access Date: 2022-06-27.”

The data underlying this article are available in the glass classification dataset, at “https://www.kaggle.com/datasets/uciml/glass: Access Date: 2022-06-27.”

The data underlying this article are available in the Bank Note Authentication UCI dataset, at “https://www.kaggle.com/datasets/ritesaluja/bank-note-authentication-uci-data: Access Date: 2022-06-27.”

The data underlying this article are available in the contraceptive method choice dataset, at “https://www.kaggle.com/datasets/faizunnabi/contraceptive-method-choice: Access Date: 2022-06-27.”

Figure 13. Efficiency estimation of designed intelligent hybrid incomplete mixed data clustering model over clustering approaches regarding (a) Accuracy, (b) DBI, and (c) AS for dataset 5.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.