A Novel k-Means Algorithm for Clustering and Outlier Detection

Zhou, Yinghua; Yu, Hong; Xue-mei, Cai

doi:10.1109/fitme.2009.125

Cited by 24 publications

(6 citation statements)

References 16 publications

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“…Combining inequalities ( 21) and ( 22), we can obtain: (23) which means that the problem ( 16) has a lower bound. Thus, in each iteration, Algorithm 1 can monotonically decrease the objective function values of problem ( 6) until the algorithm converges.…”

Section: B Convergence Analysismentioning

confidence: 99%

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

Xu,

Han,

et al. 2019

Preprint

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Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Among various clustering approaches, the family of K-Means algorithms gains popularity due to simplicity and efficiency. However, most of existing K-Means based clustering algorithms cannot deal with outliers well and are difficult to efficiently solve the problem embedded the L0-norm constraint. To address the above issues and improve the performance of clustering significantly, we propose a novel clustering algorithm, named REFCMFS, which develops a L2,1-norm robust loss as the data-driven item and imposes a L0-norm constraint on the membership matrix to make the model more robust and sparse flexibly. In particular, REFCMFS designs a new way to simplify and solve the L0-norm constraint without any approximate transformation by absorbing• 0 into the objective function through a ranking function. These improvements not only make REFCMFS efficiently obtain more promising performance but also provide a new tractable and skillful optimization method to solve the problem embedded the L0-norm constraint. Theoretical analyses and extensive experiments on several public datasets demonstrate the effectiveness and rationality of our proposed REFCMFS method.

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Section: B Convergence Analysismentioning

confidence: 99%

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

Xu,

Han,

et al. 2019

Preprint

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“…Besides, GBS is also effective for undersampling of unbalanced classification. In addition, the time complexity of GBS is O(n), so it can speed up most classifiers [39].…”

Section: Granular-ball Samplingmentioning

confidence: 99%

“…The time complexity of k-means is O(N kt) [39], where k represents the number of clusters, and t represents the number of iterations. The convergence speed of k-means is fast and can be considered approximately linear.…”

Section: Time Complexitymentioning

confidence: 99%

An Efficient and Adaptive Granular-ball Generation Method in Classification Problem

Xia¹,

Dai²,

Wang³

et al. 2022

Preprint

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Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granularball generation using the division to replace k-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granular-ball's overlap eliminating and some other factors. This makes the granular-ball generation process of parameter-free and completely adaptive in the true sense. In addition, this paper first provides the mathematical models for the granular-ball covering. The experimental results on some real data sets demonstrate that the proposed two granular-ball generation methods have similar accuracies with the existing method while adaptiveness or acceleration is realized. All codes have been released in the open source GBC library at http://www.cquptshuyinxia.com/GBC.html.

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“…In [6], the outlier detection using in-degree number (ODIN) algorithm, -means and 'Outlyingness factor' have been used to remove one or more data points (outliers) and get non-overlapping clusters. In [7] proposed the approach of finding the clusters  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol. 24, No.…”

Section: Introductionmentioning

confidence: 99%

A simple, effective distance and density based outlier detection algorithm

Sajidha

Agarwal

Pruthviraj

et al. 2021

IJEECS

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Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.

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A Novel k-Means Algorithm for Clustering and Outlier Detection

Cited by 24 publications

References 16 publications

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

An Efficient and Adaptive Granular-ball Generation Method in Classification Problem

A simple, effective distance and density based outlier detection algorithm

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