In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected edges in a subgraph. This measure is ada...
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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 a...
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Density peak clustering (DPC) algorithm has been cited and further improved by many researchers since it was put forward. In the aspect of cluster centers discovery, the locations of these points need manually judged ...
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Divide-and-Merge is a methodology for clustering a set of objects that combines a top-down "divide" method with a bottom-up "merge" method. In this paper, we propose a 2-way normalized cut with aut...
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In this paper we introduce an effective and unified approach to creating quality video abstractions. The research was motivated by a recently developed subspace learning method called 2D-LPP, or two-dimensional Locali...
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ISBN:
(纸本)1595937331
In this paper we introduce an effective and unified approach to creating quality video abstractions. The research was motivated by a recently developed subspace learning method called 2D-LPP, or two-dimensional Locality Preserving Projection, which proved to be effective for dimensionality reduction and discriminating enough in 'appearance-based' image recognitions. By exploiting temporal constraints (sequential correlations / contextual content) inherent in a video (vs. random collection of static images) and the use of two 2D-LPP in tandem, an image in the original extremely high (m×n)-dimensional pixel-based image space Im×n is transformed into a point in the compact (d×d)-dimensional feature subspace fd×d, with (d m) and (d m×n stay close in f d×d and the intrinsic geometry and local structure of the original data are preserved. The feature subspace then lends itself easily to a conventional data clustering technique to identify suitably scattered but temporally connected clusters. If necessary, a global visual colour descriptor can also be used, so the distance metric in clustering incorporates both global and local characteristics. From the clusters, which satisfy some cluster-validity constraints and user requirements (e.g., the number of clusters, most stable or most dynamic content, etc), a summary storyboard of the video is created, comprising pertinent video frames whose features are closest to the centroid of each cluster, for content browsing and search purposes. Experiments on various videos show that the summarisation results are very encouraging when compared with manually acquired 'ground truth'. Copyright 2007 ACM.
Data clustering partitions a dataset into clusters where each cluster contains similar data. clustering algorithms usually require users to set the number of clusters, e.g., k-means or fuzzy c-means. However, it is di...
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The Mapper algorithm, a technique within topological data analysis (TDA), constructs a simplified graphical representation of high-dimensional data to uncover its underlying shape and structural patterns. The algorith...
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For many clustering applications, Multi-view data sets are very common. Multi-view clustering aims to exploit information across views instead of individual views, which is promising to improve clustering performance....
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The most important step in hierarchical clustering is to find a pair of clusters with the highest degree of similarity to merge. A widely used evolutionary tree reconstruction algorithm in computational biology, Neigh...
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Lingo algorithm is a clustering method based on latent semantic analysis based on STC algorithm. The main disadvantages of this algorithm are as follows: First, the time efficiency of SVD is too low, and there is a ce...
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