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International Conference on data science and Advanced analytics (DSAA)

作者： Poonam Goyal Sonal Kumari Shubham Singh Vivek Kishore Sundar S. Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory Pilani Campus INDIA

ISBN: (纸本)9781509052073

Clustering is a popular data mining and machine learning technique which discovers interesting patterns from unlabeled data by grouping similar objects together. Clustering high-dimensional data is a challenging task as points in high dimensional space are nearly equidistant from each other, rendering commonly used similarity measures ineffective. Subspace clustering has emerged as a possible solution to the problem of clustering high-dimensional data. In subspace clustering, we try to find clusters in different subspaces within a dataset. Many subspace clustering algorithms have been proposed in the last two decades to find clusters in multiple overlapping subspaces of high-dimensional data. Subspace clustering algorithms iteratively find the best subset of dimensions for a cluster from 2d-1 possible combinations in d-dimensional data. Subspace clustering is extremely compute intensive because of exhaustive search of subspaces, especially in the bottom-up subspace clustering algorithms. To address this issue, an efficient parallel framework for grid-based bottom-up subspace clustering algorithms is developed, considering popular algorithms belonging to this category. The framework is implemented for shared memory, distributed memory, and hybrid systems and is tested for three grid-based bottom-up subspace clustering algorithms: CLIQUE, MAFIA, and ENCLUS. All parallel implementations exhibit impressive speedup and scalability on real datasets.

关键词： Conferences

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Preface

ICMLCA 2021 - 2nd International Conference on Machine Learni...

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ICMLCA 2021 - 2nd International Conference on Machine Learning and computer Application 2021年 iii页

作者： Ning, Xiansheng Feng, Yongxin Zhang, Wenbo Yue, Mingkai Du, Ke-Lin Huang, Shutao Zhou, Fang Ba, Shuhong Fu, Chong Shui, Penglang Xu, Shuwen Wu, Sheng Wang, Shushan Du, Zhonghua Gao, Zhijun Fan, Chunlong Li, Yufeng Wang, Yupeng Liu, Qingli Yu, Jingang Yin, Zhenyu Zhang, Deyu Gao, Hongwei Wen, Feng Guo, Cean Hao, Yongping Chan, Yung-Kuan Pavlovich, Vinogradov Gennady Kirpichnikova, Irina Qian, Zhihong Ivanov, Dmitriy V. Zhou, Xueguan Nezamabadi-Pour, Hossein Yang, Qing Zhang, Yongtang Liao, Haibin Huang, Ying Srinivasan, Kathiravan Xu, Zhenghua Pachori, Ram Bilas Zhang, Jialu AlShawabkeh, Mahmoud Guo, Chengjun Chaugule, Archana Ajit Lima, Alberto Sampaio Liu, Huayong Vidal, Jorge Maestre Alrshah, Mohamed A. Ghafoor, Kayhan Zrar Kubicek, Jan Chaugule, Archana Ajit Rodríguez-Pérez, Miguel Kumar, Gulshan Nasri, Mehdi Borrego, Carlos Persico, Valerio Wagh, Sanjeev Yan, Wenjun Yang, Li Yang, Dawei Xing, Yongkang Yang, Huadong Shang, Chengguo Chang, Xingong Shi, Hongbo Li, Aijun Feng, Liping Lv, Yali Liang, Minfu Wang, Kaixuan Bai, Zengliang Liang, Anhui Nan, Zhihong Ji, Suqin Chang, Liwei Zhou, Hongbin Yao, Chunli Cao, Guodong Zhang, Juling School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Concordia University Canada School of Automotive and Transportation Shenyang Ligong University China School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Northeastern University China Xidian University China Beijing University of Posts and Telecommunications China Beijing Institute of Technology China Nanjing University of Science & Technology China Shenyang Jianzhu University China Shenyang Aerospace University China Dalian University China Shenyang Institute of Computing Technology Chinese Academy of Sciences China Shenyang Ligong University China National Chun Hsing University Taiwan Department of Informatics and Applied Mathematics Russia South Ural State University Russia Jilin University China Samara State University of Transport Russia Naval Universit of Engineering China Shahid Bahonar University of Kermanto Iran Central China Normal University China Guangdong Neusoft Institute China Hubei University of Science and Technology China Liuzhou Railway Vocational Technical College China Vellore Institute of Technology India Hebei University of Technology China Indian Institute of Technology Indore India Xiangnan University China Guangxi Normal University for Nationalities China UESTC China Pimpri-Chinchwad College of Engineering & Research India Federal University of Ceará Brazil Complutense University of Madrid Spain Universiti Putra Malaysia Malaysia Shanghai Jiao Tong University China VŠB - Technical University of Ostrava Cuw Pimpri-Chinchwad College of Engineering & Research Ravet Pune India University of Vigo Spain Division of Research and Development India Islamic Azad University Iran Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Opportunistic Networks United States Univers

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Modeling the homophily effect between links and communities for overlapping community detection 25

Modeling the homophily effect between links and communities ...

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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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