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4th IEEE International Conference on Information Science and Technology (ICIST)

作者： Zhang, Hongyan Yu, Xiaoming Zhai, Yi Shandong Normal Univ Sch Management Sci & Engn Jinan Shandong Peoples R China Shandong Jianzhu Univ Sch Civil Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781479948086

The hierarchical clustering algorithm is one of the clustering algorithms used widely. The typical character of the hierarchical clustering algorithm is to find the "shortest" patterns. But it is not to ensure that to find the global optimal result by adding the shortest pattern each time. In this paper, we use DNA computing to solve this problem. Because during the ligation of the DNA strands, the bio-reaction's character is to combine all the possible single DNA strands into the shortest double DNA strand. We propose a DNA model with the coding strategy and DNA computing algorithm for the hierarchical clustering problem. we use this algorithm to solve the real problem-the retailer's region partition problem. Although we use the computer to simulate the bio-chemical reactions, we will execute this algorithm in the biological lab in the near future.

关键词： DNA computing hierarchical clustering algorithm DNA model

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hierarchical Management of Large-Scale Malware Data 2

Hierarchical Management of Large-Scale Malware Data

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IEEE International Conference on Big Data

作者： Kellogg, Lee Ruttenberg, Brian O'Connor, Alison Howard, Michael Pfeffer, Avi Charles River Analyt 625 Mt Auburn St Cambridge MA 02138 USA

ISBN: (纸本)9781479956661

As the pace of generation of new malware accelerates, clustering and classifying newly discovered malware requires new approaches to data management. We describe our Big Data approach to managing malware to support effective and efficient malware analysis on large and rapidly evolving sets of malware. The key element of our approach is a hierarchical organization of the malware, which organizes malware into families, maintains a rich description of the relationships between malware, and facilitates efficient online analysis of new malware as they are discovered. Using clustering evaluation metrics, we show that our system discovers malware families comparable to those produced by traditional hierarchical clustering algorithms, while scaling much better with the size of the data set. We also show the flexibility of our system as it relates to substituting various data representations, methods of comparing malware binaries, clustering algorithms, and other factors. Our approach will enable malware analysts and investigators to quickly understand and quantify changes in the global malware ecosystem.

关键词： Big Data data analysis invasive software pattern classification pattern clustering Big Data approach clustering evaluation metrics data representation global malware ecosystem hierarchical clustering algorithm hierarchical data management hierarchical malware organization large-scale malware data malware analysis malware binaries malware classification malware clustering malware geneation algorithm design and analysis Big data clustering algorithms Databases Malware Organizations

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A Biclustering Approach for Investigating Patterns for Breast Cancer Attributes

A Biclustering Approach for Investigating Patterns for Breas...

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International Conference on Intelligent Computing Applications (ICICA)

作者： Selvi, M. Bhuvaneswari, V. Bharathiar Univ Dept Comp Applicat Coimbatore Tamil Nadu India

ISBN: (纸本)9781479939664

Clinical data is generated in large volume in hospital sector where analysis of clinical experimental data for diagnosis of disease for doctors becomes essential. Data mining concepts are used extensively for analysis of clinical attributes. Biclustering algorithms are commonly applied to group clustering with different conditions simultaneously. The objective of this paper is to analyse breast cancer clinical dataset using biclustering approach. Breast cancer has become one of the threatening diseases to women in India. This paper uses biclustering algorithms to find the attribute clusters. The experimental results found that the identified biclustering patterns helps to find patients who are affected with breast cancer based on the attribute patterns.

关键词： Biclustering clustering hierarchical clustering algorithm Breast cancer

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Estimating Incremental Dimensional algorithm with Sequence Data Set

Estimating Incremental Dimensional Algorithm with Sequence D...

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International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME)

作者： Adaekalavan, S. JJ Coll Arts & Sci Dept Informat Technol Pudukkottai Tamil Nadu India

ISBN: (纸本)9781467358453;9781467358439

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. In this paper, the scholar proposes a new approach for robust hierarchical clustering based on the distance function between each data object and the cluster centers. This method avoids the need to compute the distance of each data object to the cluster center. It saves running time. The experimental results showed that the best clusters were obtained using EIDA method, this suggests that this similarity measure would be applicable to sequence data sets.

关键词： Data Mining clustering analysis Agglomerative clustering hierarchical clustering algorithm

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Identification of hierarchical and Overlapping Functional Modules in PPI Networks

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2012年第4期11卷 386-393页

作者： Wang, Jianxin Ren, Jun Li, Min Wu, Fang-Xiang Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Agr Univ Coll Informat Sci & Technol Changsha 410128 Hunan Peoples R China Univ Saskatchewan Dept Mech Engn Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 5A9 Canada

Various evidences have demonstrated that functional modules are overlapping and hierarchically organized in protein-protein interaction (PPI) networks. Up to now, few methods are able to identify both overlapping and hierarchical functional modules in PPI networks. In this paper, a new hierarchical clustering algorithm, called OH-PIN, is proposed based on the overlapping M_clusters, lambda-module, and a new concept of clustering coefficient between two clusters. By recursively merging two clusters with the maximum clustering coefficient, OH-PIN finally assembles all M_clusters into lambda-modules. Since M_clusters are overlapping, lambda-modules based on them are also overlapping. Thus, OH-PIN can detect a hierarchical organization of overlapping modules by tuning the value of lambda. The hierarchical organization is similar to the hierarchical organization of GO annotations and that of the known complexes in MIPS. To compare the performance of OH-PIN and other existing competing algorithms, we apply them to the yeast PPI network. The experimental results show that OH-PIN outperforms the existing algorithms in terms of the functional enrichment and matching with known protein complexes.

关键词： clustering coefficient hierarchical clustering algorithm overlapping and hierarchical module PPI networks

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A novel intrusion detection system based on hierarchical clustering and support vector machines

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第1期38卷 306-313页

作者： Horng, Shi-Jinn Su, Ming-Yang Chen, Yuan-Hsin Kao, Tzong-Wann Chen, Rong-Jian Lai, Jui-Lin Perkasa, Citra Dwi Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 106 Taiwan No Taiwan Inst Sci & Technol Dept Elect Engn Taipei Taiwan Natl United Univ Dept Elect Engn Miaoli Taiwan Ming Chuan Univ Dept Comp Sci & Informat Engn Tao Yuan Taiwan

This study proposed an SVM-based intrusion detection system, which combines a hierarchical clustering algorithm, a simple feature selection procedure, and the SVM technique. The hierarchical clustering algorithm provided the SVM with fewer, abstracted, and higher-qualified training instances that are derived from the KDD Cup 1999 training set. It was able to greatly shorten the training time, but also improve the performance of resultant SVM. The simple feature selection procedure was applied to eliminate unimportant features from the training set so the obtained SVM model could classify the network traffic data more accurately. The famous KDD Cup 1999 dataset was used to evaluate the proposed system. Compared with other intrusion detection systems that are based on the same dataset, this system showed better performance in the detection of DoS and Probe attacks, and the beset performance in overall accuracy. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Network intrusion detection system (NIDS) Support vector machines (SVMs) hierarchical clustering algorithm KDD Cup 1999 Network security Data mining

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A New Segment-Based algorithm for Stereo Matching

A New Segment-Based Algorithm for Stereo Matching

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IEEE International Conference on Mechatronics and Automation

作者： Liu, Zhihua Han, Zhenjun Ye, Qixiang Jiao, Jianbin Chinese Acad Sci Grad Univ Beijing Peoples R China

ISBN: (纸本)9781424426928

A new segment-based algorithm for stereo matching is put forward in this paper. Firstly, the reference image is segmented by mean shift algorithm and an adaptive window matching method using histogram of orientation gradient is presented. Secondly, a set of reliable pixels is constructed by comparing the matching cost and the mutual checking consistent between the left-right initial disparity maps, which can lead to an actual disparity plane. Thirdly, the improved hierarchical clustering algorithm is applied to merge the neighboring segment regions. Angle and distance are used together to measure whether the two planes are the same or not, which will reduce the calculation complexity. Finally, we derive the plane set in each region and the final disparity map is produced. Experiments verify the advantages.

关键词： Stereo matching Histogram of gradient Plane fitting hierarchical clustering algorithm

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A PROGRESSIVE REGION-MERGING algorithm FOR STEREO MATCHING

A PROGRESSIVE REGION-MERGING ALGORITHM FOR STEREO MATCHING

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IEEE Youth Conference on Information, Computing and Telecommunication

作者： Liu, Zhihua Ye, Qixiang Ke, Lu Jiao, Jianbin Chinese Acad Sci Grad Univ Beijing 100049 Peoples R China

ISBN: (纸本)9781424450756

A progressive region-merging algorithm for stereo matching is put forward in this paper. Firstly, the reference image is segmented by mean shift algorithm and the initial disparity maps are calculated using loopy belief propagation method. Secondly, we derive the reliable pixels through left-right consistent constraint. In addition, we present a new approach to exclude mismatch pixels in textureless regions, which will also reduce errors in the following region merging process. Finally, a progressive region-merging algorithm based on hierarchical clustering algorithm is put forward to merge neighboring regions and refine the plane parameters. Angle and distance are used together to measure whether the two planes are the same or not, which will reduce the calculation complexity. Experiments validate the proposed algorithm.

关键词： Stereo matching loopy belief propagation hierarchical clustering algorithm

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Performance comparison of gene family clustering methods with expert curated gene family data set in Arabidopsis thaliana

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PLANTA 2008年第3期228卷 439-447页

作者： Yang, Kuan Zhang, Liqing Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Virginia Tech Virginia Bioinformat Inst Blacksburg VA 24061 USA Virginia Tech Program Genet Bioinformat & Computat Biol Blacksburg VA 24061 USA

With the exponential growth of genomics data, the demand for reliable clustering methods is increasing every day. Despite the wide usage of many clustering algorithms, the accuracy of these algorithms has been evaluated mostly on simulated data sets and seldom on real biological data for which a "correct answer" is available. In order to address this issue, we use the manually curated high-quality Arabidopsis thaliana gene family database as a "gold standard" to conduct a comprehensive comparison of the accuracies of four widely used clustering methods including K-means, TribeMCL, single-linkage clustering and complete-linkage clustering. We compare the results from running different clustering methods on two matrices: the E-value matrix and the k-tuple distance matrix. The E-value matrix is computed based on BLAST E-values. The k-tuple distance matrix is computed based on the difference in tuple frequencies. The TribeMCL with the E-value matrix performed best, with the Inflation parameter (=1.15) tuned considerably lower than what has been suggested previously (=2). The single-linkage clustering method with the E-value matrix was second best. Single-linkage clustering, K-means clustering, complete-linkage clustering, and TribeMCL with a k-tuple distance matrix performed reasonably well. Complete-linkage clustering with the k-tuple distance matrix performed the worst.

关键词： Arabidopsis complete linkage gene family hierarchical clustering algorithm K-means clustering single linkage TribeMCL

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A hierarchical clustering method for semantic knowledge bases

A hierarchical clustering method for semantic knowledge base...

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11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks

作者： Fanizzi, Nicola d'Amato, Claudia Univ Bari Dipartimento Informat Campus UnivVia Orabona 4 I-70125 Bari Italy

ISBN: (纸本)9783540748281

This work presents a clustering method which can be applied to relational knowledge bases. Namely, it can be used to discover interesting groupings of semantically annotated resources in a wide range of concept languages. The method exploits a novel dissimilarity measure that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features, represented by a group of concept descriptions (discriminating features). The algorithm is an adaptation of the classic BISECTING K-MEANS to complex representations typical of the ontology in the Semantic Web. We discuss its complexity and the potential applications to a variety of important tasks.

关键词： description logics hierarchical clustering algorithm medoids Semantic Web

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