Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation

Abstract

<span>Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.</span>

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

“…In realworld applications, both numeric and categorical features are often used to define the data. Clustering analysis is one of the most important approaches in DM, and it seeks to find the nature of groupings or clusters of data objects within an attribute space [8,11,16]. For an exploratory approach, we applied clustering analysis to the dataset in Appendix B.…”

“…(32), knn (49), svm (81) and rf (69), to the right of the graph characterised by a strongly positive coordinate on the axis, to individuals such as MCDA C (58), characterised by a strongly negative coordinate on the axis (to the left of the graph). Dimension 2 opposes individuals such as lstm (54), word2vec (88), nlp (63) and BIM ( 16), who at the top of the graph, and characterised by a low positive co-ordinate on the axis, with individuals such as ann (8), adaboost (3), who have low negative coordinate on the axis and are located at the bottom of the graph. The Dim1, group 1 (dt , knn, svm and rf) is sharing high values for the variables "predicting", "supervised", "monitoring", "frequency", "institutional data", "data project-simulation-signal", "classifying", "best method and "interview-literature-text" (variables are sorted from the strongest).…”

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

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