Selection Of Centroids Research Articles

Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm

Abstract Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.

Cross domain-based ontology construction via Jaccard Semantic Similarity with hybrid optimization model

Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models.

Detection and Correction of Abnormal Data with Optimized Dirty Data: A New Data Cleaning Model

Each and every business enterprises require noise-free and clean data. There is a chance of an increase in dirty data as the data warehouse loads and refreshes a large quantity of data continuously from the various sources. Hence, in order to avoid the wrong conclusions, the data cleaning process becomes a vital one in various data-connected projects. This paper made an effort to introduce a novel data cleaning technique for the effective removal of dirty data. This process involves the following two steps: (i) dirty data detection and (ii) dirty data cleaning. The dirty data detection process has been assigned with the following process namely, data normalization, hashing, clustering, and finding the suspected data. In the clustering process, the optimal selection of centroid is the promising one and is carried out by employing the optimization concept. After the finishing of dirty data prediction, the subsequent process: dirty data cleaning begins to activate. The cleaning process also assigns with some processes namely, the leveling process, Huffman coding, and cleaning the suspected data. The cleaning of suspected data is performed based on the optimization concept. Hence, for solving all optimization problems, a new hybridized algorithm is proposed, the so-called Firefly Update Enabled Rider Optimization Algorithm (FU-ROA), which is the hybridization of the Rider Optimization Algorithm (ROA) and Firefly (FF) algorithm is introduced. To the end, the analysis of the performance of the implanted data cleaning method is scrutinized over the other traditional methods like Particle Swarm Optimization (PSO), FF, Grey Wolf Optimizer (GWO), and ROA in terms of their positive and negative measures. From the result, it can be observed that for iteration 12, the performance of the proposed FU-ROA model for test case 1 on was 0.013%, 0.7%, 0.64%, and 0.29% better than the extant PSO, FF, GWO, and ROA models, respectively.

Building a training dataset for classification under a cost limitation

PurposeA necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results.Design/methodology/approachIn this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances.FindingsExperimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both “data representativeness” and “data selection cost,” so that the training data labeled by experts can effectively establish a classifier with high accuracy.Originality/valueNo previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue.

Improved Mean Shift Algorithm for Maximizing Clustering Accuracy

Clustering is a machine learning method that can group similar data points. Mean Shift (MS) is a fixed window-based clustering algorithm, which calculates the number of clusters automatically but cannot guarantee the convergence of the algorithm. The main drawback of the Mean Shift Algorithm is that the algorithm requires to set a stopping criterion (threshold point) otherwise all clusters move towards one cluster and fixed bandwidth is used here. It cannot define the upper bound of iteration numbers and need to set the iteration numbers. This paper proposed a new Mean Shift Algorithm, called Improved Mean Shift (IMS) algorithm, which overcomes the all defined pitfalls of Mean Shift Algorithm. The IMS process KD-tree data structure was used to sort the dataset and all data points as initial cluster centroids without a random selection of initial centroids. In each iteration, it shifts the variable bandwidth sliding window to the actual data point nearest to the mean using k-nearest neighbours (kNN) algorithm and finds the number of clusters automatically. Also, this paper handles the missing values using Mean Imputation (MI). The IMS algorithm produces better results than the Mean Shift Algorithm on both synthetic and real datasets.

Optimisation of K-means algorithm based on sample density canopy

Since the random selection of the initial centroid and the artificial definition of the number of clusters affect the experimental results of K-means, therefore, this article uses sample density an...

An Indexing Algorithm Based on Clustering of Minutia Cylinder Codes for Fast Latent Fingerprint Identification

Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates' order to be compared, fingerprint indexing algorithms that reduce the search space while minimizing the increase in the error rate (compared to the identification) are developed. In the present research, we propose an algorithm for indexing latent fingerprints based on minutia cylinder codes (MCC). This type of minutiae descriptor presents a fixed structure, which brings advantages in terms of efficiency. Besides, in recent studies, this descriptor has shown an identification error rate, at the local level, lower than the other descriptors reported in the literature. Our indexing proposal requires an initial step to construct the indices, in which it uses k-means++ clustering algorithm to create groups of similar minutia cylinder codes corresponding to the impressions of a set of databases. K-means++ allows for a better outcome over other clustering algorithms because of the selection of the proper centroids. The buckets associated with each index are populated with the background databases. Then, given a latent fingerprint, the algorithm extracts the minutia cylinder codes associated with the clusters' indices with the lowest distance respect to each descriptor of this latent fingerprint. Finally, it integrates the votes represented by the fingerprints obtained to select the candidate impressions. We conduct a set of experiments in which our proposal outperforms current rival algorithms in presence of different databases and descriptors. Also, the primary experiment reduces the search space by four orders of magnitude when the background database contains more than one million impressions.

An efficient distance estimation and centroid selection based on k-means clustering for small and large dataset

In this paper an efficient distance estimation and centroid selection based on k-means clustering for small and large dataset. Data pre-processing was performed first on the dataset. For the complete study and analysis PIMA Indian diabetes dataset was considered. After pre-processing distance and centroid estimation was performed. It includes initial selection based on randomization and then centroids updations were performed till the iterations or epochs determined. Distance measures used here are Euclidean distance (Ed), Pearson Coefficient distance (PCd), Chebyshev distance (Csd) and Canberra distance (Cad). The results indicate that all the distance algorithms performed approximately well in case of clustering but in terms of time Cad outperforms in comparison to other algorithms.

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

The RBF networks belong to a set of artificial neural network architectures. RBF networks have been successfully applied for solving various data mining tasks including classification, and regression. Successful implementation of the RBF network depends on numerous factors among which, the crucial is its structure. The decision on the network structure has to be taken at the network initialization stage. It requires calculating or inducing the number of centroids, and their respective locations. The above problem is known to be NP-hard, and hence, not easily solvable. The traditional approach for deciding on the number of hidden units is based on applying the k-means algorithm for calculating cluster centroids. Unfortunately, the procedure guarantees neither a satisfactory accuracy nor the required generalization level of the RBF network under development. To alleviate the problem for cluster determination, i.e. number of centroids, we propose the similarity-based algorithm (SCA) for the RBF networks initialization, as well as an alternative method for initializing RBFNs using the kernel-based fuzzy clustering algorithm (KFCM-K). In both cases, the number of resulting centroids and their initial locations are provided automatically. The next step involves applying the optimization procedure resulting in the selection of the final centroids’ location. The procedure is integrated with the output weights determination. Since the discussed optimization problem is computationally difficult it has been decided to apply the agent-based population learning algorithm (PLA) which belongs to the class of metaheuristics. A comparative study of approaches based on SCA and KFCM-K is included in the paper. Their effectiveness is demonstrated experimentally using artificial and real benchmark datasets. The results of the computational experiment have shown that both proposed approaches for designing RBFNs perform significantly better than other algorithms used for this task.

Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering

Clustering is a technique in data mining thatgroups data sets into similar data clusters. One of thealgorithms that is commonly used for clustering is K-Means.However, the K-Means algorithm has several weaknesses, oneof them is the random factor in initial centroid selection, sothat cluster result is inconsistent even though it is tested withthe exact same data. The Modified K-Means algorithm focuseson selecting the initial centroid to overcome inconsistencies ofcluster results in the K-Means method. The test was conductedusing sentipol dataset and only focused on comment data.Furthermore, the specified number of clusters is 3 based on thenumber of existing comment labels (positive, negative, andneutral). According to testing result proves that Modified KMeans algorithm produces better purity value than K-Meansalgorithm. Modified K-Means algorithm produces average ofpurity value 0,42, while K-Means produces average of purityvalue 0,391. Meanwhile, from testing related to random factorsconducted 5 times with the same attributes and test data, theresults of the cluster on the Modified K-Means algorithm didnot change, so automatically the resulting purity value was alsothe same. Whereas in the K-Means algorithm, the clusterresults always change in each test, so the result of purity valueis also likely to change.

PENINGKATAN KINERJA ALGORITMA K MEANS DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PENGELOMPOKAN DATA PENYEDIAAN AKSES

Water is one of the things that plays a very important role in human survival, because the Indonesian government has a community-based water supply and sanitation (PAMSIMAS) program, so that all the programs run well need a regional status grouping technique in this thesis. with the K-means algorithm.
 K-means is a partition algorithm that aims to divide the data into the specified number of clusters, the results of the K means algorithm depend on the selection of the initial klater center but problems that often occur when selecting the initial centroid are randomly drawn from the solution. from the grouping is not quite right. To overcome this problem the author wants to use the PSO algorithm in the initial centroid selector for the K-means algorithm, in this study also compared the selection of the first 3 centroids according to random, second according to government standards the value of high, medium and low drinking water quality then the third method proposed by the PSO algorithm was then tested with Davies Bouldin Index.
 From the test results, the K-means method with the selection of random initial centroid with a value of 0.208856082, the K-means method with the selection of centroids in accordance with government standards about SAM conditions of 0.280077 and the best selection method is K-means PSO 0, 08383. So testing the PAMSIMAS data using K-means PSO found that the method was more optimal.

DISCERN: diversity-based selection of centroids for k-estimation and rapid non-stochastic clustering

One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-Means is one of the most efficient offline clustering algorithms, it is not equipped to estimate the number of clusters, which is useful in some practical cases. Other practical methods which do are simply too complex, as they require at least one run of K-Means for each possible K. In order to address this issue, we propose a K-Means initialization similar to K-Means++, which would be able to estimate K based on the feature space while finding suitable initial centroids for K-Means in a deterministic manner. Then we compare the proposed method, DISCERN, with a few of the most practical K estimation methods, while also comparing clustering results of K-Means when initialized randomly, using K-Means++ and using DISCERN. The results show improvement in both the estimation and final clustering performance.

An Automatic Centroid Image Selection Method Based on Fuzzy Logic Reasoning in Image Deduplication

Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.

Selecting the shape of centroids of round and non-round gears

In the design of gears, the most important task is to obtain centroids. Features of selection of centroids of round and non-round gears obtained by means of functions of harmonious form are considered, process of its formation is shown. Analytical dependencies are presented describing the geometry of the centroid of a round and non-round gearwheel. An example of an implementation of a mechanism based on dependencies describing the construction of a centroid is given.

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

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%.

Quality and size assessment of quantized images using K-Means++ clustering

In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE). K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.

Segmentation of Leaf Spots Disease in Apple Plants Using Particle Swarm Optimization and K-means Algorithm

Leaf spot disease is one of the causes of a decrease in apple production. Early detection of this disease will increase the quality and amount of apple production. Monitoring the health of apple plants in larger area is traditionally a job that requires a lot of time and effort. The use of drones to detect the leaf spot disease is an alternative technology to monitor the health of apple plants in large areas. The image of an apple leaf taken by a drone needs to be processed by segmenting the apple leaves into the infected and the healthy leaves to detect apple leaf spot disease. K-means clustering offers an algorithm that is simple, fast and works unsupervised to segment images compared to level set algorithms. Random selection of centroid will cause K-means to be trapped at the local optimum point and result in unsatisfactory image segmentation. To solve this problem, Particle Swarm Optimization offers a good solution to optimization problems to avoid convergence problems at the local optimum. Therefore, in this paper, we study how to segment leaf spot disease on apple leaves using the Particle Swarm Optimization and K-means algorithm. The objective function of K-means algorithm optimized by Particle Swarm Optimization is used for segmenting the leaf spot disease. The first step in this proposed method is to convert the leaf image from RGB to CIE L * a * b format. The a component’s of L * a * b format are taken and clustered by using the Particle Swarm Optimization. The global best of the Particle Swarm Optimization becomes an initial centroid of K-means algorithm. The experimental results show that the Particle Swarm Optimization-K-means (PSOK) has a better performance than the K-means algorithm in segmenting leaf spot disease.

Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

Fuzzy C-Means (FCM) plays a major role in brain tissue segmentation. The proposed method aims to implements rapid brain tissue segmentation from MRI human head scans using FCM in CPU and GPU. This method is known as FCM-GENIUS. This paper presents three novel steps to enrich the performance of conventional FCM algorithm in CPU. There are region of interest (ROI) selection, knowledge based initialization and knowledge based optimization. The ROI selection is a preprocessing step contains brain extraction and bounding box processes. An automatic knowledge based initialization to FCM algorithm using histogram smoothing for centroids selection from middle slice of the given MRI brain volume. Optimization helps to improve the computation speed up of FCM algorithm using MRI slice adjacency property. The materials used for the proposed work are gathering from internet brain segmentation repository (IBSR). The accuracy of segmentation also compared with traditional and existing methods. The proposed method yield equal segmentation accuracy compared with existing methods but reduces the segmentation time considerably up to seven times and average number of iterations up to three times. In addition, parallel FCM implements in GPU machine and the performance was compared with the conventional FCM in CPU. The single instruction multiple data (SIMD) model was used with the hybrid CPU–GPU implementation in the GPU machine to accelerate the medical image segmentation.

A novel Tag Score (T_S) model with improved K-means for clustering tweets

Clustering of tweets is useful for analyzing the attitudes of people towards a particular product. The companies can use this analysis to modify their products to meet the needs of people. Recently, K-means clustering is widely used to cluster the tweets with bag of words as a feature set. The key factors contributing to the quality of clusters and performance of clustering are dimensionality reduction and initial selection of centroids. This paper addresses these issues using a newly proposed Tag Score (T_S) model with improved K-means in which semantically similar features from bag of words are grouped into tags, scores are modified based on sentiment polarity values and the initial centroids are selected with the help of sentiment scores. The performance of the proposed T_S model with improved K-means is compared with T_S model with random K-means and conventional word vectors with random K-means by considering three labeled datasets and three unlabeled datasets. The results show that the proposed method produces significant results in approximately 70% of the cases in terms of purity, F-measure, intra-cluster distance and inter-cluster distance.

Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k-means (KM) and fuzzy c-means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k-means (IKM) and improved fuzzy c-means (IFCM) clustering algorithms segment the INSAT-3D satellite's thermal infrared image into low-level, middle-level, high-level clouds and non-cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non-cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms.