A novel clustering algorithm based on fitness proportionate sharing

Yan, Xuyang; Homaifar, Abdollah; Nazmi, Shabnam; Razeghi-Jahromi, Mohammad

doi:10.1109/smc.2017.8122905

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…A recently developed density-based clustering approach, namely FPS-clustering [31], is used to explore the cluster structure of data without any prior knowledge or parameter optimization. In [26], the authors extended FPS-clustering to feature clustering analysis for completely continuous or discrete features by proposing two different cluster merge schemes.…”

Section: B Density-based Feature Clustering Analysismentioning

confidence: 99%

“…The terms β refers to the normalization parameter and γ is the stabilization parameter. We adopt the same parameter estimation procedure from [31] to obtain the values of β and γ. Algorithm 1 summarizes the density-based feature clustering procedure. Algorithm 1 starts by estimating the density values of features in the F cont and F disc using equation 4.…”

Section: B Density-based Feature Clustering Analysismentioning

confidence: 99%

“…Also, let d 12 be the distance between the centers of F C tp1 and F C tp2 . The following remark defines 1 The details of the FPS strategy can found be in [31].…”

Section: B Density-based Feature Clustering Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

Yan¹,

Sarkar²,

Gebru³

et al. 2021

Preprint

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Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features. Some recent mixed-type feature selection studies only select features with high relevance to class labels and ignore the redundancy among features. The determination of an appropriate feature subset is also a challenge. In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data. SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters. Extensive experiments as well as comparison studies with five state-of-the-art methods are conducted on SFSDFC using thirteen real-world benchmark datasets and results justify the efficacy of the SFSDFC method.

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Section: B Density-based Feature Clustering Analysismentioning

confidence: 99%

Section: B Density-based Feature Clustering Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

Yan¹,

Sarkar²,

Gebru³

et al. 2021

Preprint

Self Cite

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“…Due to a depletion of resources, people will then search for the second most pleasant city and migrate to it. In an analogy to this procedure, all of the potential cluster centers can be explored with fitness proportionate sharing as detailed in [52]. After the search of potential cluster centers, a dynamic niche expansion [13], [53] is employed to remove redundant clusters, and obtain an optimal set of clusters.…”

Section: A Offline Fps-clustering Algorithmmentioning

confidence: 99%

A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

Yan

Razeghi-Jahromi²,

Homaifar

et al. 2019

IEEE Access

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As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined parameters while the characteristics of real-time data are considered unknown. Besides, the user-specified threshold is used to overcome the effect of outliers and noises, which significantly affects the clustering performance. The overlap among clusters is another major challenge for the existing stream clustering methods. These constraints strongly limit their real-time applications. In this paper, a two-phase stream clustering algorithm based on fitness proportionate sharing is proposed. It handles streaming data when prior knowledge is not available and maps the clustering problem into a multimodal optimization problem. It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers. To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm. The proposed algorithm is applied to different data sets, and a comprehensive comparison between the proposed algorithm and five well-known data stream clustering algorithms in the literature is provided. Results show comparable or better performance relative to five popular data stream clustering algorithms. A scalability analysis of the proposed streaming clustering method is presented in this paper as well.INDEX TERMS Data streams, clustering, unsupervised learning, data mining. I. INTRODUCTION

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“…In literature, a substantial amount of work on unsupervised feature selection method can be found [20][21][22]. In [23], a method is proposed that partitions the considered feature set into distinct clusters in such a way that features in a cluster are highly similar, while features in different clusters are quite dissimilar.…”

Section: Introductionmentioning

confidence: 99%

A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

Saxena¹,

Chugh²,

Sajid³

et al. 2022

AAIML

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A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.

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A novel clustering algorithm based on fitness proportionate sharing

Cited by 10 publications

References 11 publications

A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

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