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Metagenomic contigs binning is a necessary step of metagenome analysis. After assembly, the number of contigs belonging to different genomes is usually unequal. So a metagenomic contigs dataset is a kind of imbalanced dataset and traditional fuzzy c-means method (FCM) fails to handle it very well. In this paper, we will introduce an improved version of fuzzy c-means method (IFCM) into metagenomic...
Community detection is one of the most important ways that reflect the structure and mechanism beneath the social network. The overlapping communities are more in line with the reality of social network. In the society, the phenomenon of some members shared membership of different communities reflects as overlapping communities in the network. Facing big data network, it is a challenging and computationally...
Big Data are expanding fast and widely researched and used in many domains. One of the largest challenges in data mining is how to cluster the Big Data efficiently. CF-tree is the original of many big data clustering algorithms, however some shortcomings are exist. This paper proposes an algorithm named Clustering based On the Summary Statistics (COSS). We first analyzes the shortcomings of the traditional...
As a product of Web2.0, micro-blog is developing rapidly these years. More and more information spread on the micro-blog because of its high speed and convenience, social hotspots and news events included. As a result, discovering, extraction and analyzing information become researching hotspots. By studying micro-blog text and long text cluster, this article draws a conclusion that traditional cluster...
Stream data can often show abrupt changes over time. It is very critical to analyze and predict the trends autonomously on time in the continuous, high speed and variable stream data environment. In this paper, we discuss the Autonomous Real-time Clustering Evolution Tracking algorithm which integrate the fractal cluster technique, self-adaptive sampling technique with the restriction of computing...
Energy efficiency and data reliability have always been the key problem in WSN. In this paper, firstly, a selection of cluster head algorithm is designed which is able to detect the energy change of nodes, rapidly ascertain new cluster head, and avoid excessive energy consumption between cluster head and cluster nodes. Secondly, differentiated service is introduced in our algorithm which makes achieve...
On the basis of integration of rough sets (RS) and RBF Artificial neural network (ANN), a model is constructed to identify the industry clusters life cycle. Firstly, the continuous attribute values are discretized using fuzzy clustering algorithm based on maximum discernibility value (MDV) search method and information entropy. And then the major attributes are reduced by rough sets. At last, taking...
An action can be represented as a sequence of salient postures. Effective modeling of the salient postures is critical for robust action recognition. This paper proposes to characterize the salient postures using a set of the spatio-temporal interesting points (STIPs). Local features are extracted at each STIP and the statistical distribution of the features for each salient posture is further modelled...
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