A hybrid algorithm for k-medoid clustering of large data sets

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components

A novel approach for multispectral satellite image classification based on the bat algorithm

Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

A novel approach for multispectral satellite image classification based on the bat algorithm

Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

An approach based on tunicate swarm algorithm to solve partitional clustering problem

The tunicate swarm algorithm (TSA) is a newly proposed population-based swarm optimizer for solving global optimization problems. TSA uses best solution in the population in order improve the intensification and diversification of the tunicates. Thus, the possibility of finding a better position for search agents has increased. The aim of the clustering algorithms is to distributed the data instances into some groups according to similar and dissimilar features of instances. Therefore, with a proper clustering algorithm the dataset will be separated to some groups and it’s expected that the similarities of groups will be minimum. In this work, firstly, an approach based on TSA has proposed for solving partitional clustering problem. Then, the TSA is implemented on ten different clustering problems taken from UCI Machine Learning Repository, and the clustering performance of the TSA is compared with the performances of the three well known clustering algorithms such as fuzzy c-means, k-means and k-medoids. The experimental results and comparisons show that the TSA based approach is highly competitive and robust optimizer for solving the partitional clustering problems

A niching memetic algorithm for simultaneous clustering and feature selection

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data

Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms

Image segmentation is an essential technique of image processing for analyzing an image by partitioning it into non-overlapped regions each region referring to a set of pixels. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region extraction and clustering. Clustering techniques can be used for partitioning datasets into groups according to the homogeneity of data points. The present research work proposes two algorithms involving hybridization of K-Means (KM) and Fuzzy C-Means (FCM) techniques as an attempt to achieve better clustering results. Along with the proposed hybrid algorithms, the present work also experiments with the standard K-Means and FCM algorithms. All the algorithms are experimented on four images. CPU Time, clustering fitness and sum of squared errors (SSE) are computed for measuring clustering performance of the algorithms. In all the experiments it is observed that the proposed hybrid algorithm KMandFCM is consistently producing better clustering results

Secure Algorithm for File Sharing Using Clustering Technique of K-Means Clustering

In the current scenario The Security is most or of at most importance when we are talking about file transferring in networks. In the thesis, the work has design a new innovative algorithm to securely transfer the data over network. The k –means clustering algorithm, introduced by MacQueen in 1967 is a broadly utilized plan to solve the clustering problem. It classifies a given arrangement of n-information focuses in m-dimensional space into k-clusters whose focuses are gotten by the centroids. The issue with the privacy consideration has been examined, and that is the data is distributed among various gatherings and the disseminated information is to be safeguarded. In this thesis, created chucks or parts of file using the K-Means Clustering Algorithm and the individual part is encrypted using the key which is shared between sender and receiver. Further, the bunched records have been encoded by utilizing AES encryption algorithm with the introduction of private key concept covertly shared between the involved parties which gives a superior security state

Investigation Of Multi-Criteria Clustering Techniques For Smart Grid Datasets

The processing of data arising from connected smart grid technology is an important area of research for the next generation power system. The volume of data allows for increased awareness and efficiency of operation but poses challenges for analyzing the data and turning it into meaningful information. This thesis showcases the utility of clustering algorithms applied to three separate smart-grid data sets and analyzes their ability to improve awareness and operational efficiency. Hierarchical clustering for anomaly detection in phasor measurement unit (PMU) datasets is identified as an appropriate method for fault and anomaly detection. It showed an increase in anomaly detection efficiency according to Dunn Index (DI) and improved computational considerations compared to currently employed techniques such as Density Based Spatial Clustering of Applications with Noise (DBSCAN). The efficacy of betweenness-centrality (BC) based clustering in a novel clustering scheme for the determination of microgrids from large scale bus systems is demonstrated and compared against a multitude of other graph clustering algorithms. The BC based clustering showed an overall decrease in economic dispatch cost when compared to other methods of graph clustering. Additionally, the utility of BC for identification of critical buses was showcased. Finally, this work demonstrates the utility of partitional dynamic time warping (DTW) and k-shape clustering methods for classifying power demand profiles of households with and without electric vehicles (EVs). The utility of DTW time-series clustering was compared against other methods of time-series clustering and tested based upon demand forecasting using traditional and deep-learning techniques. Additionally, a novel process for selecting an optimal time-series clustering scheme based upon a scaled sum of cluster validity indices (CVIs) was developed. Forecasting schemes based on DTW and k-shape demand profiles showed an overall increase in forecast accuracy. In summary, the use of clustering methods for three distinct types of smart grid datasets is demonstrated. The use of clustering algorithms as a means of processing data can lead to overall methods that improve forecasting, economic dispatch, event detection, and overall system operation. Ultimately, the techniques demonstrated in this thesis give analytical insights and foster data-driven management and automation for smart grid power systems of the future