PCA-K-Means Based Clustering Algorithm for High Dimensional and Overlapping Spectra Signals

Zhang, Nian; Leatham, Keenan; Xiong, Jiang; Zhong, Jue

doi:10.1109/icicip.2018.8606667

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Shan et al [48] suggest that the dimensionality reduction methods can be divided into feature selection and feature transformation. Zhang et al [49] use principal component analysis for reducing dimensions before applying k-means. They conclude that the method of dimensionality reduction yields accurate results in data clustering.…”

Section: Clustering Large High Dimensional Datamentioning

confidence: 99%

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Kumar¹,

Samuel

2023

IEEE Access

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Accurately clustering large, high dimensional datasets is a challenging problem in unsupervised learning. K-means is considered to be a fast, widely used and accurate centroid based data partitioning algorithm for spherical datasets. However, its non-determinism and heavy dependence on the selection of initial cluster centers along with vulnerability to noise make it a poor candidate for clustering large datasets with high dimensionality. To overcome these, we develop a novel, nature inspired, centroid based clustering algorithm, inspired from the principles of particle physics. Our method ensures that the convergence to local optima and non-deterministic outputs are avoided. We experiment the method on large datasets of human face images. Besides, our method addresses the problem of outliers and presence of not well-separated data in these datasets. We use a deep learning model for extracting facial features into a vector of 128 dimensions. We validate the quality and accuracy of our methods using different statistical parameters like f-measure, accuracy, error rate, average in group proportion and normalized cluster size rand index. These evaluations show that our method exhibits better accuracy and quality in clustering large face image datasets, in comparison with other existing mechanisms. The strength of our algorithms is more visible as the size of the dataset grows. INDEX TERMSCentroid Based Celestial Clustering, Face clustering on large datasets, Optimization on clustering, Refined Celestial PSO Clustering

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Section: Clustering Large High Dimensional Datamentioning

confidence: 99%

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Kumar¹,

Samuel

2023

IEEE Access

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“…PCA ensures dimensionality reduction, whereas LDA reveals clusters. PCA-k-means approach is investigated in [14] for the clustering of high dimensional and overlapping signals. The approach effec-tively reduces the dimension and clusters the signals accurately.…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

et al. 2021

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Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

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“…With this classification model (algorithm), the data objects in the same cluster become more similar compared to the data objects in the other clusters. Meanwhile, the individual centroid of each cluster and the sum of squares of distances between data objects are used to create a cost function for the minimization task that will be repeated to classify and assign every data object to a certain cluster [ 5 , 14 , 15 , 16 , 17 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. The K-means algorithm is a clustering technique to classify input data into K clusters based on unsupervised learning.…”

Section: Related Researchmentioning

confidence: 99%

A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost

Jung

Lee

2020

Entropy

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Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

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PCA-K-Means Based Clustering Algorithm for High Dimensional and Overlapping Spectra Signals

Cited by 5 publications

References 8 publications

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost

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