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Due to its robustness and computational efficiency, the mean shift based algorithms have achieved considerable success in object tracking. Color histogram is used in these algorithms to represent the target model. The Bhattacharyya coefficient is employed as the similarity measure to evaluate the difference between the model and the target candidates. In practice, there exist some limits in these...
An outlier is the object which is very different from the rest of the dataset on some measure. Finding such exception has received much attention in the data mining field. In this paper, we propose a KNN based outlier detection algorithm which is consisted of two phases. Firstly, it partitions the dataset into several clusters and then in each cluster, it calculates the Kth nearest neighborhood for...
Energy conservation in ad hoc network is a challenging problem. However, simply maintain residual energy or adjust transmitting power of mobile nodes can not certainly achieve energy efficiency. In this paper, we analyze the energy consumption in the case of end-to-end and hop-by-hop retransmission by concerning with the link error. In addition, we define new link cost based on such schemes and apply...
Since an outlier often contains useful information, outlier detection is becoming a hot issue in data mining. Thus, an efficient outlier mining algorithm based on KNN is proposed in this paper. It can find outlier more accurately through defining a correlation matrix considering the importance and correlation between attributes. In addition, a data structure R-tree is used in the algorithm and it...
Presently, outlier mining is used for many areas such as telecommunication, finance and intrusion detection. However, finding outliers needs amounts of computation with most traditional algorithms. Thus, we propose a modified density based outlier mining algorithm in this paper. For every object in dataset, our algorithm need not judge whether there are core objects within the epsiv-neighborhood of...
Outlier detection is widely used for many areas such as credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction and marketing. In this paper, we demonstrate the effectiveness of spectral clustering in dataset with outliers. Through spectral method we can use the information of feature space with eigenvectors rather than that of the whole dataset to...
As a new theory for studying non-linear complex systems, fractal geometry has received much attention recently. Based on the relationship between length of curve and change of scale as well as the idea that a closed curve is formed by a certain amount of unclosed curves, we present an improved Douglas-Peuker method (IDPM) based algorithm. Our algorithm can not only keep the shape and details of the...
Outlier mining is to discover the objects with exceptional behavior in dataset. It is an important challenge from the knowledge discovery standpoint and attracts much attention recently. The density based outlier mining algorithm is an effective approach to detect anomalous points. However, such algorithms usually need amounts of computations. In this paper, we propose a modified density based detection...
Recently, spectral clustering has become one of the most popular modern clustering algorithms which are mainly applied to image segmentation. In this paper, we propose a new spectral clustering algorithm and attempt to use it for outlier detection in dataset. Our algorithm takes the number of neighborhoods shared by the objects as the similarity measure to construct a spectral graph. It can help to...
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