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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zhang, Hongyi Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.

关键词： Matrix factorization

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Hyperspherical Fuzzy clustering for online document categorization

Hyperspherical Fuzzy clustering for online document categori...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jian-Ping Mei Yangtao Wang College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Data Analytics Department Institute for Infocomm Research Singapore

ISBN: (纸本)9781509006274

For document data which are typically represented as high dimensional and sparse vectors, cosine distance based Hyperspherical Fuzzy C-Means(HFCM) has been shown to be more effective than classic Euclidean distance based fuzzy c-means(FCM) for document categorization. The existing HFCM approach assumes a static dataset and performs clustering in a batch mode. This design makes HFCM no more suitable when the dataset keeps increasing in its size or is too large to be loaded wholly. In this paper, we work on fuzzy clustering approaches for online document clustering. Specifically, we propose to perform hyperspherical fuzzy c-means in an online manner based on the stochastic gradient method. In this Online Hyperspherical Fuzzy C-Means(OnHFCM), documents are assumed to come one by one and centroids of clusters are updated immediately when each document is arrived. Such a formulation allows OnHFCM to be applicable to large datasets which may be incrementally changing in size. Two variants of OnHFCM with different objective functions are presented in this paper. In addition to the classic online algorithm which processes documents one by one, mini-batch algorithm that handles a small batch of documents at a time is also provided. Experimental results on real-world benchmarks demonstrate that the proposed OnHFCM algorithms achieved better performance than existing ones in online document categorization.

关键词： Clustering algorithms Linear programming Gradient methods Electronic mail Benchmark testing Extraterrestrial measurements Scalability

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A fast, Scalable SLINK Algorithm for Commodity Cluster Computing Exploiting Spatial Locality

A fast, Scalable SLINK Algorithm for Commodity Cluster Compu...

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IEEE International Conference on High Performance Computing and Communications

作者： Poonam Goyal Sonal Kumari Sumit Sharma Dhruv Kumar Vivek Kishore Sundar Balasubramaniam Navneet Goyal Advanced Data Analytics & Parallel Technologies Laboratory Department of Computer Science & Information Systems BITS-Pilani Pilani Campus INDIA

ISBN: (纸本)9781509042982

Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. To the best of our knowledge, all existing parallel SLINK algorithms are based on the traditional SLINK algorithm and thus require large number of computing resources. In this paper, we present a novel optimization of SLINK algorithm, GridSLINK, which is an order of magnitude faster than the existing state-of-the-art implementation. The optimization in GridSLINK comes from reduction in number of distance calculations required by SLINK. This reduction is achieved by exploiting spatial locality of data points and using an adaptive gridding technique. GridSLINK is parallelized for distributed memory systems. Scalable performance is achieved for increasing number of compute nodes. The proposed parallel algorithm, dGridSLINK, is benchmarked against the best existing parallel algorithm in literature and found to outperform the latter for all the real datasets considered. dGridSLINK can cluster millions of data points in few seconds/minutes using a small number of processing elements, without compromising the quality of clustering.

关键词： Parallel computing multi-core multi-node clustering SLINK adaptive gridding

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Computational analysis of the structural properties of economic and financial networks

arXiv

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arXiv 2017年

作者： Emmert-Streib, Frank Musa, Aliyu Baltakys, Kestutis Kanniainen, Juho Tripathi, Shailesh Yli-Harja, Olli Jodlbauer, Herbert Dehmer, Matthias Predictive Medicine and Data Analytics Lab Department of Signal Processing Tampere University of Technology Finland Institute of Biosciences and Medical Technology Tampere Finland Industrial and Information Management Tampere University of Technology Tampere Finland Institute for Systems Biology Seattle United States Institute for Intelligent Production Faculty for Management University of Applied Sciences Upper Austria Steyr Campus Austria Department for Biomedical Computer Science and Mechatronics UMIT - The Health and Lifesciences University Hall in Tyrol Austria College of Computer and Control Engineering Nankai University Tianjin300350 China

In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems. In this paper, we are surveying the use of networks and network-based methods for studying economy related questions. We start with a brief overview of graph theory and basic definitions. Then we discuss descriptive network measures and network complexity measures for quantifying structural properties of economic networks. Finally, we discuss different network and tree structures as relevant for applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Finance

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Topic modeling for social media content: A practical approach

Topic modeling for social media content: A practical approac...

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International Conference on computer and Information sciences (ICCOINS)

作者： Vala Ali Rohani Shahid Shayaa Ghazaleh Babanejaddehaki Department of Data Analytics Berkshire Media Kuala Lumpur Malaysia Faculty of Computer Science and Information Technology Univerity Purtra Malaysia Kuala Lumpur Malaysia

Perceiving the discussed topic in social media brings a great amount of value to different fields, such as marketing, security, education, and management. Topic modeling provides a powerful method for projecting text documents into topic space. In this paper, we explore an unsupervised topic modeling approach that incorporates LDA algorithm toward discovering the topics in social media content. Empirical experiments on social media datasets with 90,527 records reveal that this approach is quite effective for detecting the topic facets and extracting their dynamics over time. By analyzing the studied dataset, five main topics were discovered accurately by the presented algorithm according to the domain experts' comments. The presented model is quite general and can be applied in a wide variety of domains to automatically mining topics from the social media channels.

关键词： Social network services Computational modeling Probabilistic logic data models Resource management Hidden Markov models computers

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Extensions and enhancements for the DANNA neuromorphic architecture

Extensions and enhancements for the DANNA neuromorphic archi...

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IEEE Southeastcon

作者： Christopher Daffron Jason Chan Adam Disney Luke Bechtel Ryan Wagner Mark E. Dean Garrett S. Rose James S. Plank J. Douglas Birdwell Catherine D. Schuman Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN Computational Data Analytics Oak Ridge National Laboratory Oak Ridge TN

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic systems that have been developed for hardware implementation. They feature highly adaptive and programmable structural elements, which model artificial neural networks with spiking behavior. In this paper, we highlight the current hardware implementations of DANNA, including their features and functionalities. We conclude with future directions.

关键词： Field programmable gate arrays Neurons Clocks Programming computer architecture Biological neural networks Computational modeling

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DD-Rtree: A dynamic distributed data structure for efficient data distribution among cluster nodes for spatial data mining algorithms

DD-Rtree: A dynamic distributed data structure for efficient...

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IEEE International Conference on Big data

作者： Jagat Sesh Challa Poonam Goyal S. Nikhil Aditya Mangla Sundar S. Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Pilani Campus India Advanced Data Analytics & Parallel Technologies Laboratory Department of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Pilani Campus India

ISBN: (纸本)9781467390064

Parallelizing data mining algorithms has become a necessity as we try to mine ever increasing volumes of data. Spatial data mining algorithms like Dbscan, Optics, Slink, etc. have been parallelized to exploit a cluster infrastructure. The efficiency achieved by existing algorithms can be attributed to spatial locality preservation using spatial indexing structures like k-d-tree, quad-tree, grid files, etc. for distributing data among cluster nodes. However, these indexing structures are static in nature, i.e., they need to scan the entire dataset to determine the partitioning coordinates. This results in high data distribution cost when the data size is large. In this paper, we propose a dynamic distributed data structure, DD-Rtree, which preserves spatial locality while distributing data across compute nodes in a shared nothing environment. Moreover, DD-Rtree is dynamic, i.e., it can be constructed incrementally making it useful for handling big data. We compare the quality of data distribution achieved by DD-Rtree with one of the recent distributed indexing structure, SD-Rtree. We also compare the efficiency of queries supported by these indexing structures along with the overall efficiency of DBSCAN algorithm. Our experimental results show that DD-Rtree achieves better data distribution and thereby resulting in improved overall efficiency.

关键词： data structures Clustering algorithms data mining Indexing Distributed databases Algorithm design and analysis

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Light-weight versus heavy-weight algorithms for SAC and neighbourhood SAC 28

Light-weight versus heavy-weight algorithms for SAC and neig...

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28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015

作者： Wallace, Richard J. Insight Centre for Data Analytics Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9781577357308

This paper compares different forms of singleton arc consistency (SAC) algorithms with respect to: (i) their relative efficiency as a function of the increase in problem size and/or difficulty, (ii) the effectiveness with which they can be transformed into algorithms for establishing neighbourhood singleton arc consistency (NSAC) In such comparisons, it was found useful to distinguish two classes of SAC algorithm with the terms "light-weight" and "heavy-weight". The difference turns on the complexity of data structures required;this complexity is much greater for the heavyweight than the light-weight algorithms. The present work shows that this difference is reflected in scalability. In general, light-weight algorithms scale better than heavy-weight algorithms on both random and structured problems, although in the latter case the heavyweight algorithm SAC-3 is able to compensate for its extra overhead. In addition, it is shown that modifying heavy-weight algorithms to produce NSAC algorithms is problematic because the logic underlying their improvements, while suited to SAC, requires major revisions when carried over to neighbourhood SAC. As a result, even for problems where heavy-weight SAC algorithms are competitive with light-weight algorithms, the efficiency of the corresponding NSAC algorithms is seriously compromised. In contrast, light-weight SAC algorithms can be easily converted to efficient NSAC algorithms. These results show that heavy-weight algorithms for SAC cannot be considered as unconditional improvements on light-weight algorithms, and more importantly that more attention should be paid to the basic tradeoff between reducing the number of dominant operations (constraint checks) and minimizing extra overhead in local consistency algorithms. Copyright © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Efficiency

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Preface

引用

CEUR Workshop Proceedings 2017年 1935卷

作者： Bastianelli, Emanuele D'Aquin, Mathieu Nardi, Daniele Knowledge Media Institute Open University United Kingdom Insight Centre for Data Analytics National University of Ireland Galway Ireland Department of Computer Science Control and Management Engineering Sapienza University of Rome Italy

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