scholarly journals A New Clustering Approach for Automatic Oscillographic Records Segmentation

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6778
Author(s):  
Vitor Hugo Ferreira ◽  
André da Costa Pinho ◽  
Dickson Silva de Souza ◽  
Bárbara Siqueira Rodrigues
Keyword(s):  
Spatial Distribution ◽  
Power Systems ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Automatic Clustering ◽  
System Maintenance ◽  
Measurement Units ◽  
Clustering Approach ◽  
Transient Events

The analysis of waveforms related to transient events is an important task in power system maintenance. Currently, electric power systems are monitored by several event recorders called phasor measurement units (PMUs) which generate a large amount of data. The number of records is so high that it makes human analysis infeasible. An alternative way of solving this problem is to group events in similar classes so that it is no longer necessary to analyze all the events, but only the most representative of each class. Several automatic clustering algorithms have been proposed in the literature. Most of these algorithms use validation indexes to rank the partitioning quality and, consequently, find the optimal number of clusters. However, this issue remains open, as each index has its own performance highly dependent on the data spatial distribution. The main contribution of this paper is the development of a methodology that optimizes the results of any clustering algorithm, regardless of data spatial distribution. The proposal is to evaluate the internal correlation of each cluster to proceed or not in a new partitioning round. In summary, the traditional validation indexes will continue to be used in the cluster’s partition process, but it is the internal correlation measure of each one that will define the stopping splitting criteria. This approach was tested in a real waveforms database using the K-means algorithm with the Silhouette and also the Davies–Bouldin validation indexes. The results were compared with a specific methodology for that database and were shown to be totally consistent.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

scholarly journals Canonical PSO Based K-Means Clustering Approach for Real Datasets

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lopamudra Dey ◽  
Sanjay Chakraborty
Keyword(s):  
Data Mining ◽  
Air Pollution ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Cluster Validity ◽  
Validity Assessment ◽  
Different Types ◽  
Clustering Approach ◽  
Validity Measure

“Clustering” the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

scholarly journals A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm

2021 ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Public Dataset ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.

scholarly journals Data Analysis Using Representation Theory and Clustering Algorithms

2021 ◽  
Vol 19 ◽  
pp. 310-320
Author(s):  
Suboh Alkhushayni ◽  
Taeyoung Choi ◽  
Du’a Alzaleq
Keyword(s):  
Data Analysis ◽  
Random Forest ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Categorical Variables ◽  
Common Disease ◽  
Agglomerative Hierarchical Clustering ◽  
Data Set

This work aims to expand the knowledge of the area of data analysis through both persistence homology, as well as representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical data set. By testing the data, we tried to find the agglomerative hierarchical clustering method that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we came with a conclusion that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with random forest are the most beneficial approaches when working with categorical variables. All these tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, for the business area, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators.

scholarly journals Enhanced K-Means Clustering Algorithm Using Collaborative Filtering Approach

2017 ◽  
Vol 10 (2) ◽  
pp. 474-479
Author(s):  
Ankush Saklecha ◽  
Jagdish Raikwal
Keyword(s):  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Clustering Approach ◽  
Improving Accuracy ◽  
Accuracy Performance ◽  
Number Of Iterations ◽  
Filtering Approach ◽  
Selection Of

Clustering is well-known unsupervised learning method. In clustering a set of essentials is separated into uniform groups.K-means is one of the most popular partition based clustering algorithms in the area of research. But in the original K-means the quality of the resulting clusters mostly depends on the selection of initial centroids, so number of iterations is increase and take more time because of that it is computationally expensive. There are so many methods have been proposed for improving accuracy, performance and efficiency of the k-means clustering algorithm. This paper proposed enhanced K-Means Clustering approach in addition to Collaborative filtering approach to recommend quality content to its users. This research would help those users who have to scroll through pages of results to find important content.

ACPSO

2012 ◽  
pp. 226-238 ◽  
Author(s):  
Salima Ouadfel ◽  
Mohamed Batouche ◽  
Abdlemalik Ahmed-Taleb
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
A Priori ◽  
Optimal Number ◽  
Experimental Results ◽  
Swarm Optimization ◽  
Number Of Clusters ◽  
Automatic Clustering ◽  
Representation Scheme

In order to implement clustering under the condition that the number of clusters is not known a priori, the authors propose a novel automatic clustering algorithm in this chapter, based on particle swarm optimization algorithm. ACPSO can partition images into compact and well separated clusters without any knowledge on the real number of clusters. ACPSO used a novel representation scheme for the search variables in order to determine the optimal number of clusters. The partition of each particle of the swarm evolves using evolving operators which aim to reduce dynamically the number of naturally occurring clusters in the image as well as to refine the cluster centers. Experimental results on real images demonstrate the effectiveness of the proposed approach.

scholarly journals Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
David J. Miller ◽  
Carl A. Nelson ◽  
Molly Boeka Cannon ◽  
Kenneth P. Cannon
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Optimum Number ◽  
Clustering Methods ◽  
Real World Data ◽  
Data Set ◽  
World Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzyα-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that theα-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

scholarly journals K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021 ◽  
Vol 11 (23) ◽  
pp. 11246
Author(s):  
Abiodun M. Ikotun ◽  
Mubarak S. Almutari ◽  
Absalom E. Ezugwu
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimization Techniques ◽  
Background Information ◽  
Cluster Number ◽  
Automatic Data ◽  
Partitional Clustering ◽  
Automatic Clustering ◽  
Clustering Problems

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

scholarly journals A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm

2020 ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract Clustering, as a traditional machine learning method, is still playing a significant role in data analysis. The most of clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on manual identification of the elbow points on the visualization curve, which will lead to the experienced analysts not being able to clearly identify the elbow point from the plotted curve when the plotted curve being fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to work out a statistical metric estimating an optimal cluster number when clustering on a dataset. Firstly, the average degree of distortion obtained by Elbow method is normalized to the range of 0 to10; Secondly, the normalized results are used to calculate Cosine of intersection angles between elbow points; Thirdly, the above calculated Cosine of intersection angles and Arccosine theorem are used to compute the intersection angles between elbow points; Finally, the index of the above computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a public well-known dataset demonstrated that the estimated optimal cluster number output by our newly proposed method is better than widely used Silhouette method.

Clusterization of Russian Regions by the Level of Mortgage Developing

2019 ◽  
Vol 7 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Андрей Чуканов ◽  
Andrey Chukanov
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mortgage Lending ◽  
Optimal Number ◽  
Russian Regions ◽  
Development Environment ◽  
Optimal Method ◽  
R Programming Language ◽  
Hierarchical Clustering Algorithm ◽  
R Programming

In this article, in order to optimize the economic policy in the field of mortgagehousing lending, the clustering of Russian regions by the most optimal method was carried out and analyzed. The main limitations arising from the application of the most popular k-means clustering algorithm for analyzing mortgages are considered and ways to correct them are suggested. The regions were grouped using clustering algorithms using medians and medoids that are more resistant to outliers. A comparison was made of the results of the k-means, k-medians and k-medoids algorithms, and the optimal number of groups of regions with similar indicators in the field of mortgage lending and their relevant regions representatives were found. A hierarchical clustering algorithm based on the Ward method was used, the result of which was the use of five mortgage clusters in Russia. The study of the characteristics of these groups of regions will help in creating a mortgage policy that takes into account the peculiarities of the regions of Russia. All calculations were made in the R programming language; graphics were created in the Rstudio development environment.

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