Analysis of Variant Approaches for Initial Centroid Selection in K-Means Clustering Algorithm

Sandhya, N.; Sekar, M. Raja

doi:10.1007/978-981-10-5547-8_11

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…In unsupervised clustering algorithms like the k-means, the ground truth about the k-hyperparameter value, the number of clusters on a specific dataset, relies on the prior knowledge of the problem [21] .In most cases, there is no prior knowledge or intuition about the clustering dataset, at hand, and at times, the domain knowledge is required [22]. For this reason, it is important to use the metrics that give some intuition about the best or the optimal value of k on any clustering high dimensional dataset [3], [23]. Such a standard cluster validation process and set of internal validation metrics, is highly critical to assessing the quality of the k clusters generated as the output from the high-dimensional k-means algorithms [24], [25].…”

Section: Performance and Statistical Metrics For Evaluating Quality O...mentioning

confidence: 99%

Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering

Gikera,

Mwaura,

Maina

et al. 2023

IJAIDM

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Clustering is one of the tasks performed during exploratory data analysis with an extensive and wealthy history in a variety of disciplines. Application of clustering in computational medicine is one such application of clustering that has proliferated in the recent past. K-means algorithms are the most popular because of their ability to adapt to new examples besides scaling up to large datasets. They are also easy to understand and implement. However, with k-means algorithms, k-hyperparameter tuning is a long standing challenge. The sparse and redundant nature of the high-dimensional datasets makes the k-hyperparameter tuning in high-dimensional space clustering a more challenging task. A proper k-hyperparameter tuning has a significant effect on the clustering results. A number of state-of-the art k-hyperparameter tuning techniques in high-dimensional space have been proposed. However, these techniques perform differently in a variety of high-dimensional datasets and data-dimensionality reduction methods. This article uses a five-step methodology to investigate the trends and advances on the state of the art k-hyperparameter tuning techniques in high-dimensional space clustering, data dimensionality reduction methods used with these techniques, their tuning strategies, nature of the datasets applied with them as well as the challenges associated with the cluster analysis in high-dimensional spaces. The metrics used in evaluating these techniques are also reviewed. The results of this review, elaborated in the discussion section, makes it efficient for data science researchers to undertake an empirical study among these techniques; a study that subsequently forms the basis for creating improved solutions to this k-hyperparameter tuning problem.

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Section: Performance and Statistical Metrics For Evaluating Quality O...mentioning

confidence: 99%

Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering

Gikera,

Mwaura,

Maina

et al. 2023

IJAIDM

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“…Sekar [38] Three variant approaches for centroid initialization suitable for document clustering Yu et al [39] Proposed bi-layer k-means and tri-level k-means algorithms Kurada and Kanadam [40] Automatic Clustering Using TLBO…”

Section: Sandhya Andmentioning

confidence: 99%

MP-K-Means: Modified Partition Based Cluster Initialization Method for K-Means Algorithm

Gupta*,

Chandra

2019

IJRTE

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In k-means algorithm, initial cluster centroids are selected arbitrarily which leads to diverse formation of clusters in each run. Consequently, accuracy and performance of k-means is majorly depends on the selection of initial centroids. Thus, the initial cluster centroids shall be chosen carefully to obtain better accuracy and performance of k-means algorithm. In view of this, a new Modified Partition based Cluster Initialization method for k-means called as MP-k-means is proposed in this paper. MP-k-means is an amended version of P-k-means [1] in which the range of values of each dimension is divided into ‘k’ equi-sized partition based on arithmetic average. This division of range into ‘k’ equi-sized partition is affected by outliers present in the data. In order to remove the effect of outliers in P-k-means, the partitioning of each dimension is made based on positional average instead of arithmetic average in MP-k-means. Six popular datasets are used for empirical evaluation of the algorithms. The empirical results are compared and validated based on various external and internal clustering validation measures. The comparative results show that MP-k-means is significantly superior to the basic k-means and P-k-means. The proposed method may also be applied to other clustering algorithms which are based on the concept of selection of initial cluster centroids.

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“…Initialization method based on iterative selection for k-means is proposed in [22]. For document clustering, Sandhya and Sekar [23] proposed three different approaches for centroid initialization. Automatic Clustering Using Teaching-Learning-based optimization (TLBO) is introduced in [24].…”

Section: Introductionmentioning

confidence: 99%

HYBCIM: Hypercube Based Cluster Initialization Method for k-means

Gupta¹,

Chandra²

2019

IJITEE

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Clustering is a data processing technique that is extensively used to find novel patterns in data in the field of data mining and also in classification techniques. The k-means algorithm is extensively used for clustering due to its ease and reliability. A major effect on the accuracy and performance of the k-means algorithm is by the initial choice of the cluster centroids. Minimizing Sum of Squares of the distance from the centroid of the cluster for cluster points within the cluster (SSW) and maximizing Sum of Square distance between the centroids of different clusters (SSB) are two generally used quality parameters of the clustering technique. To improve the accuracy, performance and quality parameters of the k-means algorithm, a new Hypercube Based Cluster Initialization Method, called HYBCIM, is proposed in this work. In the proposed method, collection of k equi-sized partitions of all dimensions is modeled as a hypercube. The motivation behind the proposed method is that the clusters may spread horizontally, vertically, diagonally or in arc shaped. The proposed method empirically evaluated on four popular data sets. The results show that the proposed method is superior to basic k-means. HYBCIM is applicable for clustering both discrete and continuous data. Though, HYBCIM is proposed for k-means but it can also be applied with other clustering algorithms which are based on initial cluster centroids.

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Analysis of Variant Approaches for Initial Centroid Selection in K-Means Clustering Algorithm

Cited by 7 publications

References 7 publications

Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering

Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering

MP-K-Means: Modified Partition Based Cluster Initialization Method for K-Means Algorithm

HYBCIM: Hypercube Based Cluster Initialization Method for k-means

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