206603960
FCM-Based Clustering Algorithm Ensemble for Large Data Sets
In the field of cluster analysis, most of the available algorithms were designed for small data sets, which cannot efficiently deal with large scale data set encountered in data mining. However, some sampling-based clustering algorithms for large scale data set cannot achieve ideal result. For this purpose, a FCM-based clustering ensemble algorithm is proposed. Firstly, it performs the atom clustering algorithm on the large data set. Then, randomly select a sample from each atom as representative to reduce the data amount. And the ensemble learning technique is used to improve the clustering performance. For the complex large data sets, the new algorithm has high classification speed and robustness. The experimental results illustrate the effectiveness of the proposed clustering algorithm.
8123307
Strategies anticipating a difference in search depth using opponent-model search
In this contribution we propose a class of strategies which focus on the game as well as on the opponent. Preference is given to the thoughts of the opponent, so that the strategy under investigation might be speculative. We describe a generalization of OM search, called (D, d)-OM search, where D stands for the depth of search by the player and d for the opponent's depth of search. A known difference in search depth can be exploited by purposely choosing a suboptimal variation with the aim to gain a larger advantage than when playing the objectively best move. The difference in search depth may have the result that the opponent does not see the variation in sufficiently deep detail. We then give a pruning alternative for (D, d)-OM search, denoted by α-β 2 pruning. A best-case analysis shows that α-β 2 prunes very efficiently, comparable to the efficiency of α-β with regard to minimax. The effectiveness of the proposed strategy is confirmed by simulations using a game-tree model including an opponent model and by experiments in the domain of Othello.
7186941
A Cartoon Video Detection Method Based on Active Relevance Feedback and SVM
By analyzing the particular features of visual content for cartoon videos, 8 typical features of MPEG-7 descriptors are extracted to distinguish the cartoons from other videos. Then, a content-based video classifier is developed by combining the active relevance feedback technique and SVM for detecting the cartoon videos. The experimental results on the vast real video clips illustrate that compared with the classifier based on SVM and that based on traditional relevance feedback technique and SVM, the proposed classifier has a higher advantage of cartoon video detection.
28211985
A Fully Unsupervised Image Segmentation Algorithm Based on Wavelet-Domain Hidden Markov Tree Models
A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.
35059796
A Unified Framework for Shot Boundary Detection
According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash.
2376280
A Radar Target Multi-feature Fusion Classifier Based on Rough Neural Network
Fusing Multi-feature will benefit radar target classification with much more belief. However, since radar target attributes such as high range resolution profiles, waveforms, frequency spectra, time-frequency spectra, higher order statistics, polarization spectra and flight path are of different dimensions, it is hard to make decision by fusing multi-feature directly. Fortunately, rough set makes decision by examining the fitness of each condition attribute separately, while neural network is powerful for dealing with nonlinear problems. With radial projection of target dimension, cruising velocity and height as condition attribute, a multi-feature rough neural network fusion classifier is presented. Simulation of the proposed classifier based on an information system with 25 targets belonging to 6 classes shows accuracy not less than 93%, while attributes are subjoined with typical radar errors.
16297828
Neural Networks for Scientific Paper Classification
This paper describes an approach to the use of neural networks for improving the scientific paper classification performance. On the basis of the initial classification results obtained from the content-based naive Bayes method, this approach uses neural networks to model the citation link structures of the scientific papers for refining the class labels of the documents. The approach is examined and compared with the naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that using citation link structures, neural networks can significantly improve the system performance over the content-based naive Bayes method for all the training set sizes
17659364
Data Extraction from Semi-structured Web Pages by Clustering
This paper introduces an approach to the use of clustering for data extraction from semi-structured Web pages. A variant hierarchical agglomerative clustering (HAC) algorithm K-neighbours-HAC is developed which uses the similarities of the data format (HTML tags) and the data content (text string values) to group similar text tokens into clusters. Using these clusters, similar text tokens are identified as data fields and extracted as target information. The approach is examined and compared with a number of existing information extraction systems on two different sets of Web pages and the results suggest that the new approach is effective for Web information extraction and that it outperforms all of the existing approaches on these Web sites
10271659
Gene2Oligo: oligonucleotide design for in vitro gene synthesis
There is substantial interest in implementing a bioinformatics tool that allows the design of oligonucleotides to support the development of in vitro gene synthesis. Current protocols to make long synthetic DNA molecules rely on the in vitro assembly of a set of short oligonucleotides, either by ligase chain reaction (LCR) or by assembly PCR. Ideally, such oligonucleotides should represent both strands of the final DNA molecule. They should be adjacent on the same strand and overlap the complementary oligonucleotides from the second strand to ensure good hybridization during assembly. This implies that the thermodynamic properties of each oligonucleotide have to be consistent across the set. Furthermore, any given oligonucleotide has to be totally specific to its target to avoid the creation of incorrectly assembled sequences. We have developed Gene2Oligo (http://berry.engin.umich.edu/gene2oligo/), a web-based tool that divides a long input DNA sequence into a set of adjacent oligonucleotides representing both DNA strands. The length of the oligonucleotides is dynamically optimized to ensure both the specificity and the uniform melting temperatures necessary for in vitro gene synthesis. We have successfully designed and used a set of oligonucleotides to synthesize the Saccharomyces cerevisiae cytochrome b5 by using both LCR and assembly PCR.
14110256
Query Directed Web Page Clustering
Web page clustering methods categorize and organize search results into semantically meaningful clusters that assist users with search refinement; but finding clusters that are semantically meaningful to users is difficult. In this paper, we describe a new Web page clustering algorithm, QDC, which uses the user's query as part of a reliable measure of cluster quality. The new algorithm has five key innovations: a new query directed cluster quality guide that uses the relationship between clusters and the query, an improved cluster merging method that generates semantically coherent clusters by using cluster description similarity in additional to cluster overlap, a new cluster splitting method that fixes the cluster chaining or cluster drifting problem, an improved heuristic for cluster selection that uses the query directed cluster quality guide, and a new method of improving clusters by ranking the pages by relevance to the cluster. We evaluate QDC by comparing its clustering performance against that of four other algorithms on eight data sets (four use full text data and four use snippet data) by using eleven different external evaluation measurements. We also evaluate QDC by informally analysing its real world usability and performance through comparison with six other algorithms on four data sets. QDC provides a substantial performance improvement over other Web page clustering algorithms
32198408
Modelling Citation Networks for Improving Scientific Paper Classification Performance
This paper describes an approach to the use of citation links to improve the scientific paper classification performance. In this approach, we develop two refinement functions, a linear label refinement (LLR) and a probabilistic label refinement (PLR), to model the citation link structures of the scientific papers for refining the class labels of the documents obtained by the content-based Naive Bayes classification method. The approach with the two new refinement models is examined and compared with the content-based Naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that both refinement models can significantly improve the system performance over the content-based method for all the training set sizes and that PLR is better than LLR when the training examples are sufficient.