Controlled-sized clustering based on optimization

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.

Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing the objects into groups (clusters), such that objects in a cluster are more similar to each other than objects in other clusters. Cluster analysis has long played an important role in a wide variety of fields, such as psychology, bioinformatics, pattern recognition, information retrieval, machine learning, and data mining. Many clustering algorithms, such as K-means and Unweighted Pair Group Method with Arithmetic Mean (UPGMA), have been wellestablished. A recent research focus on clustering analysis is to understand the strength and weakness of various clustering algorithms with respect to data factors. Indeed, people have identified some data characteristics that may strongly affect clustering analysis including high dimensionality and sparseness, the large size, noise, types of attributes and data sets, and scales of attributes (Tan, Steinbach, & Kumar, 2005). However, further investigation is expected to reveal whether and how the data distributions can have the impact on the performance of clustering algorithms. Along this line, we study clustering algorithms by answering three questions: 1. What are the systematic differences between the distributions of the resultant clusters by different clustering algorithms? 2. How can the distribution of the “true” cluster sizes make impact on the performances of clustering algorithms? 3. How to choose an appropriate clustering algorithm in practice? The answers to these questions can guide us for the better understanding and the use of clustering methods. This is noteworthy, since 1) in theory, people seldom realized that there are strong relationships between the clustering algorithms and the cluster size distributions, and 2) in practice, how to choose an appropriate clustering algorithm is still a challenging task, especially after an algorithm boom in data mining area. This chapter thus tries to fill this void initially. To this end, we carefully select two widely used categories of clustering algorithms, i.e., K-means and Agglomerative Hierarchical Clustering (AHC), as the representative algorithms for illustration. In the chapter, we first show that K-means tends to generate the clusters with a relatively uniform distribution on the cluster sizes. Then we demonstrate that UPGMA, one of the robust AHC methods, acts in an opposite way to K-means; that is, UPGMA tends to generate the clusters with high variation on the cluster sizes. Indeed, the experimental results indicate that the variations of the resultant cluster sizes by K-means and UPGMA, measured by the Coefficient of Variation (CV), are in the specific intervals, say [0.3, 1.0] and [1.0, 2.5] respectively. Finally, we put together K-means and UPGMA for a further comparison, and propose some rules for the better choice of the clustering schemes from the data distribution point of view.

Forest fire is one of the most dangerous natural hazards of planet earth now. Here presenting an approach where it has been trying to analyze the danger of fire for the forests of Brazil. The dataset contains data from 1998 to 2017 for all the states of Brazil where a forest fire has been caught throughout the year. An unsupervised approach like—K-Means clustering, Fuzzy C-Means, and Apriori algorithm was used to do so. This is a large dataset containing 6454 unlabeled data, to build a model with them K-Means clustering seems helpful. It tries to build subgroups (clusters) of similar data points from a large group. Fuzzy C-Means is also an unsupervised algorithm and it’s working process is similar to K-Means clustering. By using K-Means clustering, Fuzzy C-Means, and Apriori method the regions which are in risk of fire danger were detected.

K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation is needed to reveal whether and how the data distributions can have the impact on the performance of K-means clustering. Indeed, in this paper, we revisit the K-means clustering problem by answering three questions. First, how the true cluster sizes can make impact on the performance of K-means clustering? Second, is the entropy an algorithm-independent validation measure for K-means clustering? Finally, what is the distribution of the clustering results by K-means? To that end, we first illustrate that K-means tends to generate the clusters with the relatively uniform distribution on the cluster sizes. In addition, we show that the entropy measure, an external clustering validation measure, has the favorite on the clustering algorithms which tend to reduce high variation on the cluster sizes. Finally, our experimental results indicate that K-means tends to produce the clusters in which the variation of the cluster sizes, as measured by the Coefficient of Variation(CV), is in a specific range, approximately from 0.3 to 1.0.

A robust adaptive clustering analysis method for automatic identification of clusters

Exploring the uniform effect of FCM clustering: A data distribution perspective

Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results. In this paper we propose a method called MinMax FCM to overcome this problem. A new objective function is designed in MinMax FCM to this aim. We use maximum variance of clusters as objective function. In this regard, high-variance clusters are penalized. We compare MinMax FCM with FCM in terms of sum of clusters’ variances, maximum variance of clusters, and execution time using a number of UCI datasets.

Various applications of data analysis and their effects have been reported recently. With the remarkable progress in classification methods, one example being support vector machines, clustering as the main method of unsupervised classification has also been studied extensively. Consequently, fuzzy methods of clustering is becoming a standard technique. However, unsolved theoretical and methodological problems in fuzzy clustering remain and have to be studied more deeply. This issue collects five papers concerned with fuzzy clustering and related fields, and in all of them the main interest is methodology. Kondo and Kanzawa consider fuzzy clustering with a new objective function using q-divergence, which is a generalization of the well-known Kullback-Leibler divergence. Among different data types, they focus on categorical data. They also show the relations of different methods of fuzzy c-means. Thus, this study tends to further generalize methods of fuzzy clustering, trying to find the methodological boundaries of the capabilities of fuzzy clustering models. Kitajima, Endo, and Hamasuna propose a method of controlling cluster sizes so that the resulting clusters have an even size, which is different from the optimizing of cluster sizes dealt with in other studies. This technique enhances application fields of clustering in which cluster sizes are more important than cluster shapes. Hamasuna et al. study the validity measures of clusters for network data. Cluster validity measures are generally proposed for points in Euclidean spaces, but the authors consider the application of validity measures to network data. Several validity measures are modified and adapted to network data, and their effectiveness is examined using simple network examples. Ubukata et al. propose a new method of c-means related to rough sets, a method based on a different idea from well-known rough c-means by Lingras. Finally, Kusunoki, Wakou, and Tatsumi study the maximum margin model for the nearest prototype classifier that leads to the optimization of the difference of convex functions. All papers include methodologically important ideas that have to be further investigated and applied to real-world problems.

The stock is one of the most important instruments of finance. However, the tendency of stock always has a high level of irregularity. In stock market, the stock price moving is considered as a time series problem. Clustering method on stock data is one of the machine learning methods and it is one of the most important analysis methods of technical analysis. The aim of this project is to find an efficient unsupervised learning way to analysis the stock market data to make classification of the patterns on different stock price moving data and get useful information for investment decisions by implementing different clustering algorithms. For this aim, the research objective of this project is to compare several of clustering methods like K-means algorithm, EM algorithm, Canopy algorithm, specify the best number of clusters for each clustering method by several evaluation indexes, show the result of each clustering method and make evaluation on the results of these clustering methods on stock market data of standard S&P 500 stock marketing data. In addition, Weka 3 and Matlab are used to implement the clustering methods and evaluation program. Data visualization shows clearly that those public companies in the same cluster have similar stock price moving pattern. The experiment shows the result that K-means algorithm and EM algorithm perform effectively in stock price moving and Canopy algorithm can be used before K-means algorithm to improve the efficiency.

A K-means clustering algorithm involves partitioning of data iteratively into k clusters. It is one of the most popular data-mining algorithms [Wu et al. 2007], and is widely used in other applications, such as image processing and machine learning. However, k-means is highly time-consuming when data or cluster size is large. Traditionally, FPGAs have shown great promise for accelerating computationally intensive algorithms, but they are harder to use for acceleration if we rely on traditional HD-based design methods. The recent introduction of Altera SDK for the OpenCL high-level synthesis tool allows developers to utilize FPGA's potential without long development periods and extensive hardware knowledge. This article presents an optimized implementation of a k-means clustering algorithm on an FPGA using Altera SDK for OpenCL. Performance and power consumption is measured with various data, cluster, and dimension sizes. When compared to state-of-the-art solutions, this implementation supports larger cluster sizes, offers up to 21x speed over a CPU and is more power efficient than a GPU. Unlike previous implementations, it can deliver consistently high throughput across large or small feature dimensions given reasonable cluster sizes and large enough data size.

Clustering is a group of unsupervised statistical techniques commonly used in many disciplines. Considering their applications to fish abundance data, many technical details need to be considered to ensure reasonable interpretation. However, the reliability and stability of the clustering methods have rarely been studied in the contexts of fisheries. This study presents an intensive evaluation of three common clustering methods, including hierarchical clustering (HC), K-means (KM), and expectation-maximization (EM) methods, based on fish community surveys in the coastal waters of Shandong, China. We evaluated the performances of these three methods considering different numbers of clusters, data size, and data transformation approaches, focusing on the consistency validation using the index of average proportion of non-overlap (APN). The results indicate that the three methods tend to be inconsistent in the optimal number of clusters. EM showed relatively better performances to avoid unbalanced classification, whereas HC and KM provided more stable clustering results. Data transformation including scaling, square-root, and log-transformation had substantial influences on the clustering results, especially for KM. Moreover, transformation also influenced clustering stability, wherein scaling tended to provide a stable solution at the same number of clusters. The APN values indicated improved stability with increasing data size, and the effect leveled off over 70 samples in general and most quickly in EM. We conclude that the best clustering method can be chosen depending on the aim of the study and the number of clusters. In general, KM is relatively robust in our tests. We also provide recommendations for future application of clustering analyses. This study is helpful to ensure the credibility of the application and interpretation of clustering methods.

Deep learning has been popular for a few years, and it shows great capability on unsupervised leaning of representation. Deep belief network consists of multi layers of restricted Boltzmann machine(RBM) and a deep auto-encoder, which uses a stack architecture learning feature layer by layer. The learning rule is that one deeper layer learns more complex representations, which are the high level features of the input data, from the representations learnt by the layer before. Fuzzy C-Means(FCM) is one of the most popular clustering algorithms, which allows one piece of data belong to several clusters. In this paper the authors propose a novel clustering model, and introduce a novel clustering technique(DBNOC) which combines deep belief network and fuzzy c-means. The main idea is that: first, it clusters with the high level representations learnt by stacked RBM to produce the initial cluster center, then it uses the fine-tune step including one center holding clustering algorithm and deep auto-encoder to optimize the cluster center and membership between input data and every cluster by cross iteration. The authors use FCM clustering algorithm to fulfill the model and do experiment on both low dimensional datasets and high dimensional datasets. The experiment results suggest that the proposed deep belief network oriented clustering method is better than the standard K-Means and FCM algorithm on the test datasets. Even on high dimensional datasets, the DBNOC clustering method show more generalization. What's more, the proposed model is suitable both in theoretical and practical.

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Introduction:The segmentation method has a number of approaches, one of which is clustering. The clustering method is widely used for segmenting retinal blood vessels, especially the k-mean algorithm and fuzzy c-means (FCM). Unfortunately, so far there have been no studies comparing the two methods for blood vessel segmentation. Many studies do not explain the reason for choosing the method.Aim:This study aims to analyze the performance of the algorithms of k-means and FCM for retinal blood vessel segmentation.Methods:This research method is divided into three stages, namely preprocessing, segmentation, and performance analysis. Preprocessing uses the green channel method, Contrast-limited adaptive histogram equalization (CLAHE) and median filter. Segmentation is divided into three processes, namely clustering, thresholding and determining the region of interest (ROI). In the thresholding process, the determination of the threshold value uses two methods, namely the mean and the median. The third stage performs performance analysis using the performance parameters of the area under the curve (AUC) and statistical tests.Results:The statistical test results comparing FCM with k-means based on AUC values resulted in p-values <0.05 with a confidence level of 95%.Conclusion:Retinal vascular segmentation with the FCM method is significantly better than k-means.

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.