DOCSLIB.ORG

Efficient Clustering of Big Data Streams Efficient

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

4 ERGEBNISSE AUS DER INFORMATIK Recent advances in data collecting devices and data storage systems are continuously offering cheaper possibilities for gathering and storing increasingly bigger volumes of data. Similar improvements in the processing power and data bases enabled the acces- sibility to a large variety of complex data. Data mining is the task of extracting useful patterns and previously unknown knowledge out of this voluminous, various data. This thesis focuses on the data mining task of clustering, i.e. grouping objects into clusters such that similar objects are assigned to the same cluster while dissimilar ones are as- signed to different clusters. While traditional clustering algorithms merely considered static data, today’s applications and research issues in data mining have to deal with Marwan Hassani continuous, possibly infinite streams of data, arriving at high velocity. Web traffic data, click streams, surveillance data, sensor measurements, customer profile data and stock trading are only some examples of these daily-increasing applications. Since the growth of data sizes is accompanied by a similar raise in their dimensionalities, clusters cannot be expected to completely appear when considering all attributes to- Efficient Clustering of Big Data gether. Subspace clustering is a general approach that solved that issue by automatically finding the hidden clusters within different subsets of the attributes rather than consi- Streams dering all attributes together. In this thesis, novel methods for an efficient subspace clustering of high-dimensional data streams are presented and deeply evaluated. Approaches that efficiently combine the anytime clustering concept with the stream subspace clustering paradigm are inten- sively studied. Additionally, efficient and adaptive density-based clustering algorithms are presented for high-dimensional data streams. New algorithmic solutions for an ener- gy-efficient in-sensor-network aggregation and a light-weighted clustering are presen- ted for sensor streaming data. Novel open-source assessment framework and evaluation measures are presented for subspace stream clustering. Primarily, efficient models of advanced and complex clustering tasks are for the first time contributed for data streams. Efficient Clustering of Big Data Streams Efficient ISBN 978-3-86359-318-6 Marwan Hassani Efficient Clustering of Big Data Streams Von der Fakultat¨ fur¨ Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften genehmigte Dissertation vorgelegt von Diplom-Ingenieur Marwan Hassani aus Aleppo, Syrien Berichter: Univ.-Prof. Dr. rer. nat. Thomas Seidl Assoc. Prof. Dr. Mohamed M. Gaber Prof. Dr.-Ing. Stefan Kowalewski Tag der mundlichen¨ Prufung:¨ 26.01.2015 Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfugbar.¨ To my family who supported me with patience and love. To my beautiful daughters Yumna and Amena, without whom this thesis would have been completed one year earlier. Bibliografische Information der Deutschen Nationalbibliothek Ǣ òǣȀȀǤǤǤ ǣ ͳǤǡʹͲͳͷ Ͷ Ǧ¡ǡͳͲͲΨ Ǥ ǡ ǡʹͲͳͷ ò ǤʹͷǡͷʹͲ͹Ͷ ǣǤǦǤǡǦǣ̷ǦǤ ͻ͹ͺǦ͵Ǧͺ͸͵ͷͻǦ͵ͳͺǦ͸ ͺʹȋǤ ǡʹͲͳͷȌ Contents Abstract / Zusammenfassung 1 1 Overview 5 1.1 Introduction .............................. 5 1.2 Challenges for Stream Clustering .................. 8 1.3 Contribution and Thesis Structure .................. 11 I Energy-Efficient Aggregation and Clustering of Sensor Streaming Data 15 2 Energy-Efficient Distributed In-Network Clustering of Sensor Data 17 2.1 Motivation ............................... 18 2.2 Related Work ............................. 21 2.3 Problems Formulation ........................ 25 2.4 The EDISKCO Algorithm ....................... 28 2.5 Experimental Evaluation ....................... 36 2.6 Conclusion ............................... 40 3 Weighted Outlier-Aware K-Center Clustering of Sensor Data 41 3.1 Motivation ............................... 41 3.2 Preliminaries ............................. 42 3.3 The SenClu Algorithm ........................ 43 3.4 Experimental Evaluation ....................... 48 3.5 Conclusion ............................... 51 4 Energy-Efficient Self-Adaptive Clustering of Sensor Nodes 53 4.1 Motivation ............................... 54 4.2 Related Work ............................. 56 i ii CONTENTS 4.3 Problem Formulation ......................... 59 4.4 The ECLUN Algorithm ........................ 60 4.5 Experimental Evaluation ....................... 66 4.6 Conclusion ............................... 74 II High-Dimensional Density-Based Stream Clustering 75 5 Projected Density-based Stream Clustering 77 5.1 Motivation ............................... 77 5.2 Related Work ............................. 80 5.3 Problem Formulation and Definitions ................ 83 5.4 The PreDeConStream Algorithm ................... 92 5.5 Experimental Evaluation ....................... 96 5.6 Conclusion ............................... 109 6 Hierarchical Adaptive Density-based Stream Clustering 111 6.1 Motivation ............................... 112 6.2 Related Work ............................. 114 6.3 Preliminaries and Data Structure .................. 119 6.4 The HASTREAM Algorithm ..................... 132 6.5 Experimental Evaluation ....................... 140 6.6 Conclusion ............................... 154 III Advanced Anytime Stream Clustering 155 7 Outlier-Aware Non-Redundant Anytime Stream Clustering 157 7.1 Motivation ............................... 157 7.2 Related work ............................. 159 7.3 The LiarTree Algorithm ........................ 161 7.4 Experimental Evaluation ....................... 172 7.5 Conclusion ............................... 178 8 Anytime Subspace Stream Clustering 179 8.1 Motivation ............................... 180 8.2 Developing SubClusTree ....................... 181 8.3 The SubClusTree Algorithm ..................... 191 CONTENTS iii 8.4 Experimental Evaluation ....................... 195 8.5 Conclusion ............................... 204 IV Framework and Evaluation Measures for Stream Sub- space Clustering 205 9 The Subspace MOA Framework 207 9.1 Motivation ............................... 207 9.2 The Subspace MOA Framework ................... 209 9.3 Experimental Evaluation ....................... 213 9.4 Conclusion ............................... 217 10 Subspace Cluster Mapping Measure (SubCMM) 219 10.1 Motivation ............................... 219 10.2 Related Work ............................. 221 10.3 SubCMM: Subspace Cluster Mapping Measure ........... 225 10.4 Experimental Evaluation ....................... 227 10.5 Conclusion ............................... 234 V Summary 237 11 Summary and Future Work 239 11.1 Summary ............................... 239 11.2 Future Work .............................. 243 VI Appendices I Bibliography III Statement of Originality XIX List of Publications XXI Abstract Recent advances in data collecting devices and data storage systems are continu- ously offering cheaper possibilities for gathering and storing increasingly bigger volumes of data. Similar improvements in the processing power and data bases enabled the accessibility to a large variety of complex data. Data mining is the task of extracting useful patterns and previously unknown knowledge out of this voluminous, various data. This thesis focuses on the data mining task of cluster- ing, i.e. grouping objects into clusters such that similar objects are assigned to the same cluster while dissimilar ones are assigned to different clusters. While traditional clustering algorithms merely considered static data, today’s applica- tions and research issues in data mining have to deal with continuous, possibly infinite streams of data, arriving at high velocity. Web traffic data, click streams, surveillance data, sensor measurements, customer profile data and stock trading are only some examples of these daily-increasing applications. Since the growth of data sizes is accompanied by a similar raise in their dimen- sionalities, clusters cannot be expected to completely appear when considering all attributes together. Subspace clustering is a general approach that solved that issue by automatically finding the hidden clusters within different subsets of the attributes rather than considering all attributes together. In this thesis, novel methods for an efficient subspace clustering of high- dimensional data streams are presented and deeply evaluated. Approaches that efficiently combine the anytime clustering concept with the stream subspace clustering paradigm are intensively studied. Additionally, efficient and adap- tive density-based clustering algorithms are presented for high-dimensional data streams. New algorithmic solutions for an energy-efficient in-sensor-network ag- gregation and a light-weighted clustering are presented for sensor streaming data. Novel open-source assessment framework and evaluation measures are presented for subspace stream clustering. Primarily, efficient models of advanced and complex clustering tasks are for the first time contributed for data streams. 1 Zusammenfassung Aktuelle Entwicklungen in den Datenerfassungsgeraten¨ und Datenspeichersys- temen bieten standig¨ gunstigere¨ Moglichkeiten¨ zur Sammlung und Speicherung von großen Datenmengen. Mit steigender Rechenleistung und effizienteren Datenbanken wird der Zugang zu einer Vielzahl komplexer Daten ermoglicht.¨
Recommended publications
  • Anytime Algorithms for Stream Data Mining

    Anytime Algorithms for Stream Data Mining

    Anytime Algorithms for Stream Data Mining Von der Fakultat¨ fur¨ Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigte Dissertation vorgelegt von Diplom-Informatiker Philipp Kranen aus Willich, Deutschland Berichter: Universitatsprofessor¨ Dr. rer. nat. Thomas Seidl Visiting Professor Michael E. Houle, PhD Tag der mundlichen¨ Prufung:¨ 14.09.2011 Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfugbar.¨ Contents Abstract / Zusammenfassung1 I Introduction5 1 The Need for Anytime Algorithms7 1.1 Thesis structure......................... 16 2 Knowledge Discovery from Data 17 2.1 The KDD process and data mining tasks ........... 17 2.2 Classification .......................... 25 2.3 Clustering............................ 36 3 Stream Data Mining 43 3.1 General Tools and Techniques................. 43 3.2 Stream Classification...................... 52 3.3 Stream Clustering........................ 59 II Anytime Stream Classification 69 4 The Bayes Tree 71 4.1 Introduction and Preliminaries................. 72 4.2 Indexing density models.................... 76 4.3 Experiments........................... 87 4.4 Conclusion............................ 98 i ii CONTENTS 5 The MC-Tree 99 5.1 Combining Multiple Classes.................. 100 5.2 Experiments........................... 111 5.3 Conclusion............................ 116 6 Bulk Loading the Bayes Tree 117 6.1 Bulk loading mixture densities . 117 6.2 Experiments..........................
  • Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion

    Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion

    big data and cognitive computing Review Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion Umesh Kokate 1,*, Arvind Deshpande 1, Parikshit Mahalle 1 and Pramod Patil 2 1 Department of Computer Engineering, SKNCoE, Vadgaon, SPPU, Pune 411 007 India; [email protected] (A.D.); [email protected] (P.M.) 2 Department of Computer Engineering, D.Y. Patil CoE, Pimpri, SPPU, Pune 411 007 India; [email protected] * Correspondence: [email protected]; Tel.: +91-989-023-9995 Received: 16 July 2018; Accepted: 10 October 2018; Published: 17 October 2018 Abstract: Data growth in today’s world is exponential, many applications generate huge amount of data streams at very high speed such as smart grids, sensor networks, video surveillance, financial systems, medical science data, web click streams, network data, etc. In the case of traditional data mining, the data set is generally static in nature and available many times for processing and analysis. However, data stream mining has to satisfy constraints related to real-time response, bounded and limited memory, single-pass, and concept-drift detection. The main problem is identifying the hidden pattern and knowledge for understanding the context for identifying trends from continuous data streams. In this paper, various data stream methods and algorithms are reviewed and evaluated on standard synthetic data streams and real-life data streams. Density-micro clustering and density-grid-based clustering algorithms are discussed and comparative analysis in terms of various internal and external clustering evaluation methods is performed. It was observed that a single algorithm cannot satisfy all the performance measures.
  • An Extensible Framework for Data Stream Clustering Research with R

    An Extensible Framework for Data Stream Clustering Research with R

    JSS Journal of Statistical Software February 2017, Volume 76, Issue 14. doi: 10.18637/jss.v076.i14 Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R Michael Hahsler Matthew Bolaños John Forrest Southern Methodist University Microsoft Corporation Microsoft Corporation Abstract In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern mining. New algorithms for these types of data are proposed regularly and it is important to evaluate them thoroughly under standardized conditions. In this paper we introduce stream, a research tool that includes modeling and simu- lating data streams as well as an extensible framework for implementing, interfacing and experimenting with algorithms for various data stream mining tasks. The main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. In addition to data handling, plotting and easy scripting capabilities, R also provides many existing algorithms and enables users to interface code written in many program- ming languages popular among data mining researchers (e.g., C/C++, Java and Python). In this paper we describe the architecture of stream and focus on its use for data stream clustering research. stream was implemented with extensibility in mind and will be ex- tended in the future to cover additional data stream mining tasks like classification and frequent pattern mining.
  • Introduction to Stream: an Extensible Framework for Data Stream Clustering Research with R

    Introduction to Stream: an Extensible Framework for Data Stream Clustering Research with R

    Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R Michael Hahsler Matthew Bolaños Southern Methodist University Microsoft Corporation John Forrest Microsoft Corporation Abstract In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern mining. New algorithms for these types of data are proposed regularly and it is important to evaluate them thoroughly under standardized conditions. In this paper we introduce stream, a research tool that includes modeling and simu- lating data streams as well as an extensible framework for implementing, interfacing and experimenting with algorithms for various data stream mining tasks. The main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. In addition to data handling, plotting and easy scripting capabilities, R also provides many existing algorithms and enables users to interface code written in many program- ming languages popular among data mining researchers (e.g., C/C++, Java and Python). In this paper we describe the architecture of stream and focus on its use for data stream clustering research. stream was implemented with extensibility in mind and will be ex- tended in the future to cover additional data stream mining tasks like classification and frequent pattern mining. Keywords: data streams, data mining, clustering. Note: A previous version of this manuscript was published in the Journal of Statistical Soft- ware (Hahsler, Bolaños, and Forrest 2017a).
  • Optimizing Data Stream Representation: an Extensive Survey on Stream Clustering Algorithms

    Optimizing Data Stream Representation: an Extensive Survey on Stream Clustering Algorithms

    Bus Inf Syst Eng 61(3):277–297 (2019) https://doi.org/10.1007/s12599-019-00576-5 STATE OF THE ART Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms Matthias Carnein • Heike Trautmann Received: 20 June 2018 / Accepted: 13 November 2018 / Published online: 21 January 2019 Ó Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019 Abstract Analyzing data streams has received consider- 1 Introduction able attention over the past decades due to the widespread usage of sensors, social media and other streaming data Cluster analysis is an unsupervised learning technique sources. A core research area in this field is stream clus- which aims to find groups of similar objects. It is a com- tering which aims to recognize patterns in an unordered, mon tool to support decision makers by structuring the infinite and evolving stream of observations. Clustering can large and high-dimensional data into manageable groups be a crucial support in decision making, since it aims for an and thus generating an optimized data representation. optimized aggregated representation of a continuous data Common application scenarios include identifying prof- stream over time and allows to identify patterns in large itable market segments, anomaly and fraud detection or and high-dimensional data. A multitude of algorithms and sensor analysis. Most clustering algorithms require a fixed approaches has been developed that are able to find and set of data and evaluate each point multiple times to gen- maintain clusters over time in the challenging streaming erate the clusters. In practice, however, many systems are scenario. This survey explores, summarizes and categorizes continuously generating new observations.

  • Clustering Dynamic Data Streams Using Enhanced Birch Technique

    ISSN(Online): 2319-8753 ISSN (Print): 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology (A High Impact Factor, Monthly, Peer Reviewed Journal) Visit: www.ijirset.com Vol. 8, Issue 9, September 2019 Clustering Dynamic Data Streams Using Enhanced Birch Technique Shahini N1, Arthinivasini R.B2 Asst. Professor, Dept. of Computer Science & Engineering, Panimalar Engineering College, Chennai, India1 Asst. Professor, Dept. of Computer Science & Engineering, Panimalar Engineering College, Chennai, India2 ABSTRACT:Conventional clustering algorithms which are been used in the Data mining concept, using k-means algorithm doesn't estimate well for very large datasets or data streams. The data are too huge such that the data doesn't fit into the main memory and have to be stored in a distributed manner. Hence conventional clustering algorithms typically requires repeated access to all the data through more than one passes which would be very expensive when large data are to be used. BIRCH is an approach, where it has the ability to incrementally as well as dynamically cluster the incoming and multi-dimensional metric data based on the traversal of complete binary tree, where the degree of leaf node is same. The subclusters which are the child of leaf nodes can have more than two nodes which are called as clusters in an attempt to produce the best quality clustering for a given set of resources such as memory and time constrains. By using this hierarchical clustering concept, the algorithm makes the entire usage of the available memory to derive the finest possible sub-clusters with minimized I/O costs.
  • Clustering Large Datasets Using Data Stream Clustering Techniques

    Clustering Large Datasets Using Data Stream Clustering Techniques

    Clustering Large Datasets using Data Stream Clustering Techniques Matthew Bolanos˜ 1, John Forrest2, and Michael Hahsler1 1 Southern Methodist University, Dallas, Texas, USA. 2 Microsoft, Redmond, Washington, USA. Abstract. Unsupervised identification of groups in large data sets is important for many ma- chine learning and knowledge discovery applications. Conventional clustering approaches (k- means, hierarchical clustering, etc.) typically do not scale well for very large data sets. In recent years, data stream clustering algorithms have been proposed which can deal efficiently with potentially unbounded streams of data. This paper is the first to investigate the use of data stream clustering algorithms as light- weight alternatives to conventional algorithms on large non-streaming data. We will discuss important issue including order dependence and report the results of an initial study using several synthetic and real-world data sets. 1 Introduction Clustering very large data sets is important for may applications ranging from finding groups of users with common interests in web usage data to organizing organisms given genetic sequence information. The data are often not only so large that they do not fit into main memory, but they even have to be stored in a distributed manner. Conventional clustering algorithms typically require repeated access to all the data which is very expensive in a scenario with very large data. Data stream clustering has become an important field of research in recent years. A data stream is an ordered and potentially unbounded sequence of objects (e.g., data points representing sensor readings). Data stream algorithms have been developed in order to process large volumes of data in an efficient manner using a single pass over the data while having only minimal storage overhead requirements.
  • Data Stream Clustering for Large Data Sets

    Data Stream Clustering for Large Data Sets

    A Study of the Efficiency and Accuracy of Data Stream Clustering for Large Data Sets Michael Hahsler & Matthew Bola~nos Intelligent Data Analysis Group Southern Methodist University 2013 INFORMS Annual Meeting Minneapolis Convention Center, October 6{9, 2013 This work is supported in part by the U.S. National Science Foundation as a research experience for undergraduates (REU) under contract number IIS-0948893. Hahsler/Bola~nos (IDA@SMU) Data Stream Clustering INFORMS 2013 1 / 31 Table of Contents 1 Motivation 2 Conventional Clustering 3 Data Stream Clustering 4 R-extension package stream 5 Empirical Study Hahsler/Bola~nos (IDA@SMU) Data Stream Clustering INFORMS 2013 2 / 31 Problems: Conventional clustering methods become very expensive. Large data are noisy. Investigate data stream clustering methods. Motivation Clustering large data sets is important for many applications: Cluster customers for segmentation (Amazon, Netflix, etc.) Cluster documents to organize the information space (PubMed, arXiv, etc.) Cluster meteorological data to categorize phenomena (e.g., storms) Cluster genetic sequences for classifying organisms. Data is typically large with respect to Number of objects Number of expected clusters Dimensionality Hahsler/Bola~nos (IDA@SMU) Data Stream Clustering INFORMS 2013 3 / 31 Motivation Clustering large data sets is important for many applications: Cluster customers for segmentation (Amazon, Netflix, etc.) Cluster documents to organize the information space (PubMed, arXiv, etc.) Cluster meteorological data to categorize phenomena (e.g., storms) Cluster genetic sequences for classifying organisms. Data is typically large with respect to Number of objects Number of expected clusters Dimensionality Problems: Conventional clustering methods become very expensive. Large data are noisy.
  • Data Stream Clustering: a Survey

    Data Stream Clustering: a Survey

    Data Stream Clustering: A Survey JONATHAN A. SILVA, ELAINE R. FARIA, RODRIGO C. BARROS, EDUARDO R. HRUSCHKA, and ANDRE C. P. L. F. DE CARVALHO University of S~aoPaulo and JOAO~ P. GAMA University of Porto Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream cluster- ing algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with non-stationary, un- bounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this paper, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually-employed experimental methodologies. A number of references is provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Infor- mation regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed. Categories and Subject Descriptors: I.5.3 [Pattern Recognition]: Clustering|Algorithms General Terms: Algorithms Additional Key Words and Phrases: Data stream clustering, online clustering. 1.