An Attributes Similarity-Based K-Medoids Clustering Technique in Data Mining

This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index.

The proposed work demonstrates the application of Bee Colony Optimization BCO technique for the tuning of Fractional Order Proportional-Integral-Derivative FOPID controller for Three Interacting Tank system. FOPID controller parameters are composed of proportionality constant, integral constant, integral order, derivative constant and derivative order. Grunwald-Letnikov definition is used for the defining the derivative controller and Oustaloup's filter technique is used for the approximation of the function. Tuning FOPID controller parameters is more complicated as it involves a five dimensional search. Tuning is effected using an evolutionary optimization technique, the bee colony optimization so as to minimize the Integral Time Absolute Error ITAE. The proposed technique is used to control three interacting tank process. The proposed FOPID controller tuned using Bee colony optimization technique may serve as an alternative for the tuning of the fractional order controllers.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

Any data in the real world can be organized properly by Clustering the data using some clustering techniques and in these clustering techniques Fuzzy C-Means (FCM) is a very recent and better technique that can mold the data with good logic and in a highly accurate manner. FCM is same as K-Means clustering technique but FCM is developed with some Fuzzy means. FCM is associated with some constraints as FCM is more responsive as on the order how the clusters were arranged in the beginning of applying the technique. Even though it has its own defects when dealing with large data, it cannot get the finest solution as the data need some optimization? So to get some better accuracy, In our proposed paper we are applying an enhanced FCM for clustering and for obtaining precise results we are involving GSA based on gravitation laws and perception of masses. This optimizing technique (GSA) can be used to improve the limitations in order to gain a well-defined system performance; GSA can efficiently deal with many propositions of large data. Here we are going to develop a Map-Reduce mechanism to effectively deal with large data. The expected result can be achieved by choosing the better finest candidates and grouping them in the reduction mode to get the finest solution. Evaluation of practical results will be optimizing when compared to other existing similar techniques.

As one of the most widely used clustering techniques, the fuzzy k -means (FKM) assigns every data point to each cluster with a certain degree of membership. However, conventional FKM approach relies on the square data fitting term, which is sensitive to the outliers with ignoring the prior information. In this paper, we develop a novel and robust fuzzy k -means clustering algorithm, namely, joint learning of fuzzy k -means and nonnegative spectral clustering with side information. The proposed method combines fuzzy k -means and nonnegative spectral clustering into a unified model, which can further exploit the prior knowledge of data pairs such that both the quality of affinity graph and the clustering performance can be improved. In addition, for the purpose of enhancing the robustness, the adaptive loss function is adopted in the objective function, since it smoothly interpolates between l1 -norm and l2 -norm. Finally, experimental results on benchmark datasets verify the effectiveness and the superiority of our clustering method.

Welded structures are widely used in many engineering load carrying structures such as columns, towers for wind turbine or water tanks, offshore and submarine structures, girders, stiffened doors, etc. welding is a costly fabrication process and proper sequence and welding process effect the cost of a huge structure. Keeping this in view two different problems from the literature for the cost optimization of welded structures are considered in this paper. The optimization procedure is carried out using artificial bee colony (ABC) optimization technique. Classical ABC is modified to increase the convergence rate of the original algorithm. Comparison of both the variants is experimented on many bench mark examples from the literature and also on two cost optimization problems of welded structures. The results of the considered techniques are compared with the previously published results. The considered techniques have given much better results in comparison to the previously tried approaches and also modified ABC has shown superiority over classical ABC.

The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a popular data mining method due to its powerful exploratory nature and its usefulness as a preprocessing step for other data mining techniques. This article develops two novel clustering algorithms for time series data that are extensions of a crisp c-shapes algorithm. The two new algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes plus (FCS+) replaces the inner product norm in the FCM model with a shape-based distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based distance, and also replaces the FCM cluster centers with shape-extracted prototypes. Numerical experiments on 48 real time series data sets show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency. Four external cluster validity indices (the Rand index, Adjusted Rand Index, Variation of Information, and Normalized Mutual Information) are used to match candidate partitions generated by each of the studied algorithms. All four indices agree that for these finite waveform data sets, FCS++ gives a small improvement over FCS+, and in turn, FCS+ is better than the original crisp c-shapes method. Finally, we apply two tests of statistical significance to the three algorithms. The Wilcoxon and Friedman statistics both rank the three algorithms in exactly the same way as the four cluster validity indices.

Cervical dysplasia is the second most cause of the death in the females. A Pap smear test is the most efficient and prominent screening method for the detection of dysplasia in cervical cells. Pap smear is time-consuming, and sometimes, it is an erroneous method. Automated and semi-automated systems can be used for cervical cancer diagnosis and treatment. In our proposed approach, we are segmenting image first; the RGB image transformed into L * a * b * format. Then, using K-means clustering technique image has segmented into background and cytoplasm. Thresholding and the morphological operations have used to segment nucleus only from the second cluster. The shape-based features of the nucleus have been extracted. In the classification phase, fuzzy C-mean (FCM) has been used for clustering. Principle component analysis (PCA) is used to find the most prominent features. The classification of Pap smear images is based on the Bethesda system. The approach has performed on a dataset obtained from pathologic laboratory containing 150 Pap smear images. Performance evaluation has been done using Rand index (RI). The RI of fuzzy C-mean is 0.933, and using PCA, it is 0.95.

D-Bees: A novel method inspired by bee colony optimization for solving word sense disambiguation

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

In the field of pattern recognition, the study of the gene expression profiles for different tissue samples over different experimental conditions has became feasible with the arrival of micro-array based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of micro-array technology. In this article we have presented a multi-objective optimization ( MOO ) based clustering technique utilizing AMOSA ( Archived Multi-Objective Simulated Annealing ) as the underlying optimization strategy for classification of tissue samples from cancer data sets. As objective functions three cluster validity indices namely, XB, PBM, and FCM indices are optimized simultaneously to form more accurate clusters of tissue samples. The presented clustering technique is evaluated for two open source benchmark cancer data sets, which are Brain tumor data set and Adult Malignancy data set. In order to evaluate the quality or goodness of produced clusters two cluster quality measures viz, Adjusted Rand Index ( ARI ) and Classification Accuracy ( %CoA ) are calculated for each data set. Comparative results of the presented clustering algorithm with 10 state-of-the-art existing single-objective, multi-objective based clustering algorithms are shown for two benchmark data sets.

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

Feature choice involves characteristic a set of the foremost helpful options that produces compatible results because the original entire set of options. A feature choice rule is also evaluated from each the potency and effectiveness points of read. Whereas the potency considerations the time needed to search out a set of options, the effectiveness is expounded to the standard of the set of options. Supported these criteria, an economical Fuzzy C Means (FCM) is projected and by experimentation evaluated in this paper. The quick rule works in 2 steps. Within the commencement, options area unit divided into clusters by exploitation graph-theoretic cluster ways. Within the second step, the foremost representative feature that's powerfully associated with target categories is chosen from every cluster to create a set of options. Options in numerous clusters area unit comparatively freelance; the clustering-based strategy of quick incorporates a high chance of manufacturing a set of helpful and independent options. To make sure the potency of quick, we have a tendency to adopt the economical Fuzzy C Means (FCM) cluster technique. The potency associated effectiveness of the quick rule area unit evaluated through an empirical study. in depth experiments area unit dole out to match quick and a number of other representative feature choice algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with relevancy four kinds of well- known classifiers, namely, the chance primarily based Naive Thomas Bayes, the tree-based C4.5, the instance- based IB1,and also the rule-based liquidator before and once feature choice. The results, on thirty five in public on the market real-world high-dimensional image, microarray, and text knowledge, demonstrate that the quick not solely produces smaller subsets of options however additionally improves the performances of the four kinds of classifiers. Index Terms: feature subset selection, relevance, redundancy and high dimensionality.

GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game

Fuzzy C-Means (FCM) clustering technique is among the most effective partitional clustering algorithms available in the literature. The Capacitated Vehicle Routing Problem (CVRP) is an important industrial logistics and managerial NP-hard problem. Cluster-First Route-Second Method (CFRS) is one of the efficient techniques used to solve CVRP. In CFRS technique, customers are first divided into clusters in the first phase, then each cluster is solved independently as a Traveling Salesman Problem (TSP) in the second phase. This research is concerned with the clustering phase of CFRS, and TSP is then solved using a traditional optimization method. Three supervised FCM based techniques are proposed to solve the clustering phase at reduced cost via centroids (pre-FCM) initialization phase. The proposed pre-FCM initialization techniques are developed to be problem dependent. Hence, three initialization techniques are first developed using K-means technique, spatially equally distributed, and demand weighted center of mass. Then, a modified demand weighted fuzzy c-means objective function is employed to assign customers to clusters. To compare the performance of the proposed supervised FCM techniques, forty-two CVRP benchmark problems are solved using the traditional fuzzy C-means algorithm and the developed algorithms. Extensive comparisons are conducted between the traditional fuzzy C-means algorithm, the three proposed initialization techniques, and other fuzzy C-means techniques available in the literature. Results show that the proposed three initialization techniques are efficient in terms of solution quality and computational cost.