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The visual inspection can help to reveal patterns that would be computationally rather difficult to reveal. The aim of this paper is to present a program to visualize DNA sequence and cluster analysis this sequence. The program visualizes either whole given sequence, or some fragments of it uses track-based visualization. Entire system is implemented in the programming language C#.
An improved shots clustering key-frame extraction algorithm based on entropy is presented. Using the color information in the video frames, the algorithm looks every frame of a shot as a special sample and selects appropriate feature. And then through the improvement of the clustering analysis of video sequences to acquire the center value of various classes and the membership degree of every sample...
The recent interest in Wireless Sensor Networks has led to a number of clustering algorithms that use the limited energy available at sensors more efficiently. In this article, we present a heritable clustering algorithm based on HEED for low energy wireless sensor network. It brings in appointment mechanism to reduce the cluster head reelect overhead efficiently. In the cluster, TDMA communication...
The K-NN query algorithm is a widely used algorithm in the spatial database, effectively carrying on K-NN query algorithm has become the focus problem of the spatial database, the traditional query algorithm will adopt the measurement distance and pruning strategy to improve the query performance by using the tree index, it needs to carry on a large amount of distance calculation to exclude the unnecessary...
Researched on the characteristic of the traffic flow, a new method of abnormal data detection on traffic flow based on the curve-fitting is presented. First, the historical data on traffic flow are divided into the traffic flow with high speed and the traffic flow with low speed by the clustering analysis. Then, the algorithm on the determination safety zone scope is obtained by the curve-fitting...
Advantages of None Euclidean Relational Fuzzy C-means (NERFCM) is analysed, by which four Fuzzy C-means (FCM) clustering algorithms are compared, which includes Fuzzy C-means (FCM) and traditional Relational Fuzzy C-means (RFCM) and None Euclidean Relational Fuzzy C-means (NERFCM) and Any Relational Fuzzy C-means (ARFCM). Their common points and different limitations on usage are discussed, finally...
IDS (Intrusion Detection system) is an active and driving defense technology. This paper mainly focuses on intrusion detection based on data mining. The aim is to improve the detection rate and decrease the false alarm rate, and the main research method is clustering analysis. The algorithm and model of ID are proposed and corresponding simulation experiments are presented. Firstly, a method to reduce...
Presently, in the data mining scenario clustering of large dataset is one of the very important techniques widely applied to many applications including social network analysis. Applying more specific pre-processing method to prepare the data for clustering algorithms is considered to be a significant step for generating meaningful segments. In this paper we propose an innovative clustering technique...
This paper proposes a new method to cluster law texts based on referential relation of laws. We extract law entities (an entity represents a law) and their referential relation from law texts. Then SimRank algorithm is applied to calculate law entity's similarity through referential relation and law clustering is carried out based on the SimRank similarity. This is the first time to apply SimRank...
This paper presents a method of automatic acquisition threshold value that changes dynamically with data distribution by polynomial fitting technique. The proposed method overcomes the sensitivity to cluster centers and locality of FCM (fuzzy c-means) algorithm and establishes an index mechanism on the basis of above analysis. Simulations show that the adaptable fuzzy clustering image indexing performs...
Currently, a large number of clustering algorithms are available for data mining. But it will be difficult for people who to a large extent know little about data mining to select an appropriate clustering algorithm. In order to solve this problem, in this paper, we first comprehensively analyze a number of clustering algorithms, then summarize their evaluation criteria and apply the so-called fuzzy...
Business processes describe business operations of an organization and capture business requirements. Business applications provide automated support for an organization to achieve business objectives. Software architecture represents the gross structure of a business application and shows the distribution of business requirements among software components. However, mainstream design approaches rely...
In the field of data mining, clustering is one of the important methods. K-Means is a typical distance-based clustering algorithm; 2-tier clustering should implement scalable clustering by means of dividing, sampling and knowledge integrating. Among those tools of distributed processing, Map-Reduce has been widely embraced by both academia and industry. Hadoop is an open-source parallel and distributed...
The investigation of community structures in networks is an important issue in many domains and disciplines.Closely communicating community is different from the traditional community which emphasize particularly on structure or context. The definition of closely communicating community and measuring method are introduced firstly. Based on the previous work, the closely communicating community detection...
In recent years, research on dictionary design for sparse representation (SR) has changed from pre-defined to training. A Hierarchical K-means Clustering (HKC) dictionary training algorithm is proposed in this paper. The algorithm presents a framework for SR for a class of images. HKC used K-means clustering to generate atoms which is one to one corresponding to hyperplanes for approximating hyperspherical...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show...
In the k means clustering algorithm right value of clusters (k) are initially unknown and effective selections of initial seed are also difficult. In this paper efficient k-means algorithm is proposed and implemented which overcome initial seed problem and unknown number of cluster problem. The algorithm is applied on real BIST server log data and Gaussian dataset to test its accuracy and efficiency...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
Combining virtual machine technology and network computing technology will be able to effectively aggregate the widely distributed heterogeneous and autonomous resources in the Internet. This paper proposes a virtual machine server aggregation algorithm, called DVSA, based on hierarchical clustering method for virtual computing environment. According to network latencies, the algorithm clusters virtual...
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