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To overcome the deficiencies of traditional K-means algorithm whose clustering effect and stability are easily affected by the initial clustering centers, this paper proposes an initial clustering center selection algorithm based on Max-Min criterion and FLANN. The algorithm firstly identifies K farthest objects, and then finds out the k nearest neighbors of K objects respectively. Finally, take the...
We compare the performance of three parallel clustering algorithms: Canopy, K-means and fuzzy K-means in real cluster environments. By constructing cluster platform of different scale, we compare these algorithms from three metrics: run time, speedup and sizeup. Experimental results show that: (1) if both the data set and the number of nodes in the cluster are the same, both the runtime and the sizeup...
The k-means initialization technique for a wireless sensor network is a newly emerging area for researchers. There are many constraints in designing the wireless sensor network. The primary constraint is energy consumption. Clustering is used for improving the lifetime of the system by reducing the power consumption. The most popular clustering technique is k-means algorithm but it exhibits local...
Swarm Intelligence algorithms, in many optimization problems, have constantly served a purpose of global search method. One of the problems confronted during optimization is clustering problem. Input for a clustering process is a set of data which are then organized into a number of sub-groups. Modern studies have recommended that partitioned or segregated clustering algorithms are more appropriate...
The brain-computer interface (BCI), identify brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper identification of electroencephalogram (EEG) based BCI for motor imagery (MI) task is done through asynchronous approach. Transferring the brain computer interface (BCI) from laboratory state to real time application desires BCI...
Data clustering analysis is the process of finding similarity between data that are assigned into homogeneous groups and the most heterogeneous as possible among groups. There are several analysis methods in wich K-means clustering algorithm is the widly used in different research areas. Therefore, this paper reviews the most known variants of clustering methods which are K-means, IRP-K-means and...
Clustering data are one of the key issues in data mining that has attracted much attention. One of the famous algorithms in this field is K-Means clustering that has been successfully applied to many problems. But this method has its own disadvantages, such as the dependence of the efficiency of this method to initialization of cluster centers. To improve the quality of K-Means, hybridization of this...
Serial execution of K-means algorithm on large dataset takes more execution time and does not give accurate results. Parallel processing is one of the ways to improve the performance of K-Means algorithm. But the execution time and accuracy is largely dependent on selection of initial cluster centers. In this paper, parallel processing of K-Means is proposed using an initialization method to originate...
With the hypothesis of Gaussian distribution of patterns, K-means and its extensions are good for clustering. As the representative of partitional clustering algorithm, K-means follows rules for running: numbers of clusters to be set, cluster initialization to be specified and certain objective function to be optimized. In general, FCM, ANN, EM share the identical idea with K-means in the beginning...
Data Mining and High Performance Computing are two broad fields in Computer Science. The k-Means Clustering is a very simple and popular data mining algorithm that has its application spread over a very broad spectrum. MapReduce is a programming style that is used for handling high volume data over a distributed computing environment. This paper proposes an improved and efficient method to implement...
Many centroid-based clustering algorithms cannot guarantee convergence to global optima and suffer in local optimal cluster center because they are sensitive to outliers and noise. A heuristic optimal technique like particle swarm optimization (PSO) can find global optimal solution with the cost of extensive computation. In this paper, a PSO based clustering algorithm (PSOBC) has been proposed to...
This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space...
Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA)...
In this paper, the author used K-means and fuzzy K-means to analyze the classification of precipitation in JingDeZhen City, and the results showed that using fuzzy k-means algorithm is a more efficient data clustering algorithm, with better value of promotion and practical application.
Distributed clustering is a new research field of data mining now. In this paper, one of distributed clustering named DCBKC (distributed clustering based on K-means and coarse-grained parallel genetic algorithm) based on K-means and coarse-grained parallel genetic algorithm is advanced. The algorithm can solve local clustering problem of distributed clustering effectively, reflect all of local data...
In this paper we propose a clustering method based on combination of the particle swarm optimization (PSO) and the k-mean algorithm. PSO algorithm was showed to successfully converge during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, k-means algorithm can achieve faster convergence to optimum solution. At the same time,...
The fuzzy c-means/ISODATA algorithm is usually described in terms of clustering a finite data set. An equivalent point of view is that the algorithm clusters the support points of a finite-support probability distribution. Motivated by recent work on the hard version of the algorithm, this paper extends the definition to arbitrary distributions and considers asymptotic properties. It is shown that...
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