A multidisciplinary ensemble algorithm for clustering heterogeneous datasets

Abstract

Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multidisciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECA*) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multiple-featured datasets. The ECA* is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECA* against five conventional approaches: K-means (KM), K-means++ (KM++), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clustering++ (GENCLUST++). That the end, 32 heterogeneous and multiple-featured datasets are used to examine their performance using internal and external and basic statistical performance clustering measures and to measure how their performance is sensitive to five features of these datasets (cluster overlap, the number of clusters, cluster dimensionality, the cluster structure, and the cluster shape) in the form of an operational framework. The results indicate that the ECA* surpasses its counterpart techniques in terms of the ability to find the right clusters. Significantly, compared to its counterpart techniques, the ECA* is less sensitive to the five properties of the datasets mentioned above. Thus, the order of overall performance of these algorithms, from best performing to worst performing, is the ECA*, EM, KM++, KM, LVQ, and the GENCLUST++. Meanwhile, the overall performance rank of the ECA* is 1.1 (where the rank of 1 represents the best performing algorithm and the rank of 6 refers to the worst-performing algorithm) for 32 datasets based on the five dataset features mentioned above.

Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

“…ECA * is a ensemble technique primarily used for clustering divergent datasets [2] . The proposed algorithm is mainly based on backtracking search optimisation algorithm (BSA) [4 , 5] , social class ranking, levy flight optimisation, quartiles and percentiles, and Euclidean distance.…”

“…The benchmarking datasets are well-known data and publicly accessible in [1] . The clustering benchmark datasets used with their dimensions and data properites are presented in [2] .…”

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

“…Figure 3 illustrates the flow of producing the concept hierarchies using adaptive ECA* with the aid of WordNet. Since the introduced approach was based on multi-disciplinary techniques, it can, therefore, be highly effective [4]. After FCA applied to the pruned word pairs, a formal context is constructed.…”

“…One of the evolutionary clustering algorithms proposed recently in [4,5] is called ECA*. In this approach, various techniques are combined, such as classification of the social classes; percentiles and quartiles; optimisation algorithm operators, and K-means algorithm.…”

“…This method is considered as one of the popular evolutionary techniques that outperformed its counterpart algorithms due to its recombined strategy of mutation and crossover [5,6]. Meanwhile, one of the most significant parts of ECA* is the mut-over operator [4]. The core strength of ECA* is the exploitation of the mut-over operator as a recombination approach (mutation and crossover), which is extracted from BSA.…”