Genetic Algorithm-based Clustering Research Articles

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

Estimation of rainfall quantile is an important step in regional frequency analysis for planning and design of any water resources project. Related evaluations of accuracy and uncertainty help to further assist in enhancing the reliability of design estimates. In this study, therefore, we investigate the accuracy and uncertainty of regional frequency analysis of extreme rainfall computed from genetic algorithm-based clustering. Uncertainty assessment is explored with prediction of quantiles with a new spatial Information Transfer Index (ITI) and Monte Carlo simulation framework. And, accuracy assessment is done with the comparison of regional growth curves to at-site analysis for each homogenous region. Further, uncertainty assessment with the ITI method is compared with Maximum Likelihood estimation (MLE) optimized by a genetic algorithm (GA) to check the suitability of the method. Results obtained suggest the ITI-based uncertainty assessment for regional estimates outperformed those of at-site estimates. The MLE-GA method based on at-site estimates was found to be better than at-site estimates based on L-moments, suggesting the former as a better alternative to compare with regional frequency estimates. Moreover, minimal bias and least deviation of the regional growth curve were obtained in the rainfall regions. The confidence intervals of regional estimates were seen to be well within the bounds of normality assumptions. Doi: 10.28991/cej-2021-03091762 Full Text: PDF

EGA-FMC: enhanced genetic algorithm-based fuzzy k-modes clustering for categorical data

Categorical data clustering is the unsupervised technique of grouping similar objects which have categorical attributes. We propose a genetic algorithm-based fuzzy k-modes categorical data clustering algorithm using multi-objective rank-based selection with enhanced elitism operation. Compactness of the clusters and inter-cluster separation were chosen as objectives to be optimised. During elitism, in every iteration, the best parent chromosomes were identified. The entire population was passed through the selection, crossover and mutation steps. The worst children were then replaced by the best parents. Our method was evaluated on three real-world datasets and resulted in clusters of better quality as compared to current methods with a significant reduction in computation time. Additionally, statistical significance tests were conducted to show the superiority of our approach over other clustering solutions.

GENETIC ALGORITHM-BASED CLUSTERING OF WIRELESS SENSOR NETWORK WITH NOVEL DATA ENCRYPTION

Wireless sensor networks (WSNs) have been used widely in so many applications. It is the most efficient way to monitor the information. There areso many ways to deploy the sensors. Many problems are not identified and solved. The main challenge of WSN is energy efficiency and information security. WSN power consumption is reduced by genetic algorithm-based clustering algorithm. Information from cluster head to base station may have a lot of chances to get hacked. The most reliable way to manage energy consumption is clustering, and encryption will suit best for information security. In this paper, we explain clustering techniques and a new algorithm to encrypt the data in the network.

Modified genetic algorithm-based clustering for probability density functions

ABSTRACTThis article modifies two internal validity measures and applies them to evaluate the quality of clustering for probability density functions (pdfs). Based on these measures, we propose a new modified genetic algorithm called GA-CDF to establish the suitable clusters for pdfs. The proposed algorithm is tested by four numerical examples including two synthetic data sets and two real data sets. These examples illustrate the superiority of proposed algorithm over some existing algorithms in evaluating the internal or external validity measures. It demonstrates the feasibility and applicability of the GA-CDF for practical problems in data mining.

GACN: Self-Clustering Genetic Algorithm for Constrained Networks

Extending the lifespan of a wireless sensor network is a complex problem that involves several factors, ranging from device hardware capacity (batteries, processing capabilities, and radio efficiency) to the chosen software stack, which is often unaccounted for by the previous approaches. This letter proposes a genetic algorithm-based clustering optimization method for constrained networks that significantly improves the previous state-of-the-art results, while accounting for the specificities of the Internet engineering task force, Constrained RESTful Environment (CoRE), standards for data transmission and specifically relying on CoRE interfaces, which fit this purpose very well.

A New Heuristic for the Data Clustering Problem

This paper presents a new heuristic for the data clustering problem. It comprises two parts. The first part is a greedy algorithm, which selects the data points that can act as the centroids of well-separated clusters. The second part is a single-solution-based heuristic, which performs clustering with the objective of optimizing a cluster validity index. Single-solution-based heuristics are memory efficient as compared with population-based heuristics. The proposed heuristic is inspired from evolutionary algorithms (EAs) and consists of five main components: 1) genes; 2) fitness of genes; 3) selection; 4) mutation operation; and 5) diversification. The attributes of the centroids of clusters are considered as genes. The fitness of a gene is a function of two factors: 1) difference between its value and the same attribute of the mean of the data points assigned to its cluster and 2) the frequency with which it has been mutated in previous iterations. The genes that have low fitness values should be updated through the mutation operation. The mutation operation performs small change (positive or negative) in the value of the gene. The mutants are accepted if they are better (with respect to objective function) than their parents. However, diversification in the search process is maintained by allowing, with a small probability, the mutants to replace their parents even they are not better than them. The objective functions used in the proposed heuristic are Calinski Harabasz index and Dunn index. The proposed algorithm has been experimented using real-life numeric data sets of UCI repository. The number of data points and number of attributes in the datasets lie between 150–11 000 and 4–60, respectively. The results indicate that the proposed algorithm performs better than two standard EAs: 1) simulated annealing algorithm and 2) differential evolution algorithm and a genetic algorithm-based clustering method.

A genetic algorithm-based clustering and two-scan labelling for colour image segmentation

In this paper, we present a two-step segmentation method. The first consists of colour image quantisation by genetic algorithm-based clustering method. The second consists of connected component labelling CCL of quantised image. In the first step, we use real codification of chromosomes and a variable string length to adjust the number of colours of the reduced palette. We use a fitness function with a smallest number of parameters to improve run time. Once pixels are classified in 3D colour space, a two-scan CCL adapted to colour image is proposed and applied to the 2D image plan to get separate regions. To reduce regions number, small regions are grouped with the nearest surrounding regions according to their colour feature. Segmentation results are discussed in RGB and lab colour spaces.

Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm

As the conventional evolutionary clustering optimization methods are often time-consuming and easy to trap in local optimal value in dealing with the problem of change detection.Furthermore,it can not detect the edge accurately for SAR images.We proposed a method for change detection in SAR images based on the clustering analysis.The proposed method takes gray-levels as an input,uses the quantum bit to define the clustering center,searches the optimal cluster center using the quantum-inspired immune clonal algorithm,and gets the global threshold.Finally,the change-detection map is produced.Compared with KI threshold,it can achieve a better value.Compared with Genetic Algorithm Based Clustering(GAC),the proposed method can search a much better clustering center quickly and effectively.Besides,it can detect the accurate edge and improve the change detection accuracy.

Two-Phase Clustering Algorithm for Complex Distributed Data

提出了一种用于复杂分布数据的二阶段聚类算法(two-phase clustering,简称TPC),TPC 包含两个阶段:首先将数据划分为若干个球形分布的子类,每一个子类用其聚类中心代表该类内的所有样本;然后利用可以处理复杂分布数据的流形进化聚类(manifold evolutionary clustering,简称MEC)对第1 阶段得到的聚类中心进行类别划分;最后综合两次聚类结果整理得到最终聚类结果.该算法基于改进的K-均值算法和MEC 算法.在进化聚类算法的基础上引入流形距离,使得算法能够胜任复杂分布的数据聚类问题.同时,算法降低了引入流形距离所带来的计算量.在分布各异的7 个人工数据集和7 个UCI 数据集测试了二阶段聚类算法,并将其效果与遗传聚类算法、K 均值算法和流形进化聚类算法做了比较.实验结果表明,无论对于简单或复杂、凸或非凸的数据,TPC 都表现出良好的聚类性能,并且计算时间与MEC 相比明显减少.;In this paper, a Two-Phase Clustering (TPC) for the data sets with complex distribution is proposed. TPC contains two phases. First, the data set is partitioned into some sub-clusters with spherical distribution, and each clustering center represents all the members of its corresponding cluster. Then, by utilizing the outstanding clustering performance of the Manifold Evolutionary Clustering (MEC) for acomplex distributed data, the clustering centers obtained in the first phase are clustered. Finally, based on these two clustering results, the final results are obtained. This algorithm is based on an improved K-means, and the MEC. Manifold distance is introduced in evolutionary clustering to make the algorithm competent for the clustering of complex data sets. At the same time, the novel method reduces the computational cost brought by manifold distance. Experimental results on seven artificial data sets and seven UCI data sets with different structure show that the novel algorithm has the ability to identify clusters with simple or complex, convex, or non-convex distribution efficiently, compared with the genetic algorithm-based clustering, the K-means algorithm, and the manifold evolutionary clustering. Furthermore, TPC outperforms MEC obviously in terms of computational time.

SST clustering for winter precipitation prediction in southeast of Iran: Comparison between modified K-means and genetic algorithm-based clustering methods

In this paper, two innovative approaches for temporal clustering of sea surface temperature (SST) data using genetic algorithm (GA) and K-means clustering methods are introduced. In both methods, new approaches have been developed to consider the relationship between variations of the variable being clustered (SST in this study) and another climatic variable (precipitation in this study) in selection of clusters. In the case study, these models are used for clustering SST in selected geographical zones in Gulf of Oman, Arabian Sea, and the northern part of Indian Ocean with respect to the precipitation variations in the selected rain gauges in Sistan and Baluchestan Province in southeast of Iran. For this purpose, fitness function of the GA model and Euclidean distance in the modified K-means method have been formulated to minimize the variance of the precipitation data associated with each selected cluster. Application of these modified clustering methods in the case study has resulted in temporal classification of SST data which also represent below and above normal precipitation seasons in the selected rain gauges. The results of the two clustering techniques have been used for development of seasonal precipitation prediction guidelines based on the SST variations in the selected geographical zones. The results have also shown that these models can be effectively used for prediction of below and above normal precipitation seasons in the study area. Comparison between the results of this study with the official forecasts of the Islamic Republic of Iran Meteorological Organization (IRIMET) has shown significant improvements.

Summarizing Relational Data Using Semi-Supervised Genetic Algorithm-Based Clustering Techniques

Problem statement: In solving a classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association. Therefore, data transformation becomes a tedious trial-and-error work and the classification result is often not very promising especially when the number of tables and the degree of one-to-many association are large. Approach: We proposed a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables to facilitate the task of solving a classification problem in relational database. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations. It can be used in two ways. One is user-controlled clustering, where the user may control the result of clustering by varying the compactness of the spherical cluster. The other is automatic clustering, where a non-overlap clustering strategy is applied. In this study, we use the latter method to dynamically cluster multiple instances, as a means of aggregating them and illustrate the effectiveness of this method using the semi-supervised genetic algorithm-based clustering technique. Results: It was shown in the experimental results that using the reciprocal of Davies-Bouldin Index for cluster dispersion and the reciprocal of Gini Index for cluster purity, as the fitness function in the Genetic Algorithm (GA), finds solutions with much greater accuracy. The results obtained in this study showed that automatic clustering (seeding), by optimizing the cluster dispersion or cluster purity alone using GA, provides one with good results compared to the traditional k-means clustering. However, the best result can be achieved by optimizing the combination values of both the cluster dispersion and the cluster purity, by putting more weight on the cluster purity measurement. Conclusion: This study showed that semi-supervised genetic algorithm-based clustering techniques can be applied to summarize relational data with more effectively and efficiently.

Genetic algorithm-based clustering ensemble: determination number of clusters

Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering ensemble (GACE) is introduced for finding optimal clusters. The most important property of our method is the ability to extract the number of clusters. With this ability, the need for data examination is removed, and then solving related problems will not be time consuming. GACE is applied to eight series of databases. Experimental results were compared with other four clustering methods. Data envelopment analysis (DEA) is used to compare methods. The results of DEA indicate that GACE is the best method. The four methods are co-association function and average link (CAL), co-association function and K-means (CK), hypergraph-partitioning algorithm (HGPA) and cluster-based similarity partitioning (CSPA).

Immunodominance and clonal selection inspired multiobjective clustering

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-II-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

Genetic algorithm-based clustering approach for k-anonymization

k-Anonymity has been widely adopted as a model for protecting public released microdata from individual identification. This model requires that each record must be identical to at least k-1 other records in the anonymized dataset with respect to a set of privacy-related attributes. Although anonymizing the original dataset to satisfy the requirement of k-anonymity is easy, the anonymized dataset must preserve as much information as possible of the original dataset. Clustering techniques have recently been successfully adapted for k-anonymization. This work proposes a novel genetic algorithm-based clustering approach for k-anonymization. The proposed approach adopts various heuristics to select genes for crossover operations. Experimental results show that this approach can further reduce the information loss caused by traditional clustering-based k-anonymization techniques.

A genetic algorithm-based clustering approach for database partitioning

In a typical distributed/parallel database system, a request mostly accesses a subset of the entire database. It is, therefore, natural to organize commonly accessed data together and to place them on nearby, preferably the same, machine(s)/site(s). For this reason, data partitioning and data allocation are performance critical issues in distributed database application design. We are dealing with data partitioning. Data partitioning requires the use of clustering. Although many clustering algorithms have been proposed, their performance has not been extensively studied. Moreover, the special problem structure in clustering is rarely exploited. We explore the use of a genetic search-based clustering algorithm for data partitioning to achieve high database retrieval performance. By formulating the underlying problem as a traveling salesman problem (TSP), we can take advantage of this particular structure. Three new operators for GAs are also proposed and experimental results indicate that they outperform other operators in solving the TSP. The proposed GA is applied to solve the data-partitioning problem. Our computational study shows that our GA performs well for this application.

An evolutionary technique based on K-Means algorithm for optimal clustering in [formula omitted

A genetic algorithm-based efficient clustering technique that utilizes the principles of K-Means algorithm is described in this paper. The algorithm called KGA-clustering, while exploiting the searching capability of K-Means, avoids its major limitation of getting stuck at locally optimal values. Its superiority over the K-Means algorithm and another genetic algorithm-based clustering method, is extensively demonstrated for several artificial and real life data sets. A real life application of the KGA-clustering in classifying the pixels of a satellite image of a part of the city of Mumbai is provided.

Genetic algorithm-based clustering technique

A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this article. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a fixed number of clusters. The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.