Presenting a New Clustering Algorithm by Combining Intelligent Bat and Chaotic Map Algorithms to Improve Energy Consumption in Wireless Sensor Network

Asadi, Masome; Mazinani, Seyyed Majid

doi:10.1007/978-981-10-8672-4_70

Cited by 2 publications

(1 citation statement)

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“…In these approaches, new genetic operators have been introduced to obtain faster algorithms than their predecessors and achieve better quality clustering results [21]. Meanwhile, a new clustering technique was proposed to enhance energy exploitation in wireless sensor network [22]. The proposed algorithm was combined with bat and chaotic map algorithms.…”

Section: Related Workmentioning

confidence: 99%

A multidisciplinary ensemble algorithm for clustering heterogeneous datasets

Hassan

Rashid

2021

Neural Comput & Applic

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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.

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Section: Related Workmentioning

confidence: 99%