Download or read book Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories written by Berkay Aydin and published by Springer. This book was released on 2018-10-15 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief provides an overview within data mining of spatiotemporal frequent pattern mining from evolving regions to the perspective of relationship modeling among the spatiotemporal objects, frequent pattern mining algorithms, and data access methodologies for mining algorithms. While the focus of this book is to provide readers insight into the mining algorithms from evolving regions, the authors also discuss data management for spatiotemporal trajectories, which has become increasingly important with the increasing volume of trajectories. This brief describes state-of-the-art knowledge discovery techniques to computer science graduate students who are interested in spatiotemporal data mining, as well as researchers/professionals, who deal with advanced spatiotemporal data analysis in their fields. These fields include GIS-experts, meteorologists, epidemiologists, neurologists, and solar physicists.

Download or read book Data Science Concepts and Techniques with Applications written by Usman Qamar and published by Springer Nature. This book was released on 2023-04-02 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Download or read book Applications of Artificial Intelligence in Engineering written by Xiao-Zhi Gao and published by Springer Nature. This book was released on 2021-05-10 with total page 922 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence.

Download or read book Final Report written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing availability of massive observational and experimental data sets (across a wide variety of scientific disciplines) there is an increasing need to provide scientists with efficient computational tools to explore such data in a systematic manner. For example, techniques such as classification and clustering are now being widely used in astronomy to categorize and organize stellar objects into groups and catalogs, which in turn provide the impetus for scientific hypothesis formation and discovery (e.g., see Fayyad, Djorgovski and Weir (1996); or Cheeseman and Stutz (1996) or Fayyad and Smyth (1999) in a more general context). Data-driven exploration of massive spatio-temporal data sets is an area where there is particular need of data mining techniques. Scientists are overwhelmed by the vast quantities of data which simulations, experiments, and observational instruments can produce. Analysis of spatio-temporal data is inherently challenging, yet most current research in data mining is focused on algorithms based on more traditional feature-vector data representations. Scientists are often not particularly interested in raw grid-level data, but rather in the phenomena and processes which are ''driving'' the data. In particular, they are often interested in the temporal and spatial evolution of specific ''spatially local'' structures of interest, e.g., birth-death processes for vortices and interfaces in fluid-flow simulations and experiments, trajectories of extra-tropical cyclones from sea-level pressure data over the Atlantic and Pacific oceans, and sunspot shape and size evolution over time from daily chromospheric images of the Sun. The ability to automatically detect, cluster, and catalog such objects in principle provides an important ''data reduction front-end'' which can convert 4-d data sets (3 spatial and 1 temporal dimension) on a massive grid to a much more abstract representation of local structures and their evolution. In turn, these higher-level representations provide a general framework and basis for further scientific hypothesis generation and investigation, e.g., investigating correlations between local phenomena (such as storm paths) and global trends (such as temperature changes). In this work we focused on detecting and clustering trajectories of individual objects in massive spatio-temporal data sets. There are two primary technical problems involved. First, the local structures of interest must be detected, characterized, and extracted from the mass of overall data. Second, the evolution (in space and/or time) of these structures needs to be modeled and characterized in a systematic manner if the overall goal of producing a reduced and interpretable description of the data is to be met.

Download or read book Spatio temporal Data Mining to Detect Changes and Clusters in Trajectories written by Anirudh Kondaveeti and published by . This book was released on 2012 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.

Download or read book Big Data Analytics and Knowledge Discovery written by Carlos Ordonez and published by Springer. This book was released on 2018-08-20 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018, held in Regensburg, Germany, in September 2018. The 13 revised full papers and 17 short papers presented were carefully reviewed and selected from 76 submissions. The papers are organized in the following topical sections: Graph analytics; case studies; classification and clustering; pre-processing; sequences; cloud and database systems; and data mining.

Download or read book Advances in Data Mining Applications and Theoretical Aspects written by Petra Perner and published by Springer. This book was released on 2014-07-17 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th Industrial Conference on Advances in Data Mining, ICDM 2014, held in St. Petersburg, Russia, in July 2014. The 16 revised full papers presented were carefully reviewed and selected from various submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture and in process control, industry and society.

Download or read book Data Fusion and Data Mining for Power System Monitoring written by Arturo Román Messina and published by CRC Press. This book was released on 2020-06-03 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications. Features Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics Applications to a wide range of power networks are provided including distribution and transmission networks Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies Includes applications to massive spatiotemporal data from simulations and actual events

Download or read book Frequent Pattern Mining written by Charu C. Aggarwal and published by Springer. This book was released on 2014-08-29 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

Download or read book Geospatial Thinking written by Marco Painho and published by Springer Science & Business Media. This book was released on 2010-07-20 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the fourth consecutive year, the Association of Geographic Infor- tion Laboratories for Europe (AGILE) promoted the edition of a book with the collection of the scientific papers that were submitted as full-papers to the AGILE annual international conference. Those papers went through a th competitive review process. The 13 AGILE conference call for fu- papers of original and unpublished fundamental scientific research resulted in 54 submissions, of which 21 were accepted for publication in this - lume (acceptance rate of 39%). Published in the Springer Lecture Notes in Geoinformation and Car- th graphy, this book is associated to the 13 AGILE Conference on G- graphic Information Science, held in 2010 in Guimarães, Portugal, under the title “Geospatial Thinking”. The efficient use of geospatial information and related technologies assumes the knowledge of concepts that are fundamental components of Geospatial Thinking, which is built on reasoning processes, spatial conc- tualizations, and representation methods. Geospatial Thinking is associated with a set of cognitive skills consisting of several forms of knowledge and cognitive operators used to transform, combine or, in any other way, act on that same knowledge. The scientific papers published in this volume cover an important set of topics within Geoinformation Science, including: Representation and Visualisation of Geographic Phenomena; Spatiotemporal Data Analysis; Geo-Collaboration, Participation, and Decision Support; Semantics of Geoinformation and Knowledge Discovery; Spatiotemporal Modelling and Reasoning; and Web Services, Geospatial Systems and Real-time Appli- tions.

Download or read book MOBILITY MINING IN MOVING OBJECT TRAJECTORIES written by Dr. Sajimon Abraham, Dr. P Sojan Lal and Dr. Dais George and published by Lulu.com. This book was released on with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining written by Jiawei Han and published by Morgan Kaufmann. This book was released on 2022-07-02 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data

Download or read book Multiple Aspect Analysis of Semantic Trajectories written by Konstantinos Tserpes and published by Springer Nature. This book was released on 2020-01-01 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification.

Download or read book Data Engineering and Intelligent Computing written by Suresh Chandra Satapathy and published by Springer. This book was released on 2017-05-31 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a compilation of high-quality scientific papers presented at the 3rd International Conference on Computer & Communication Technologies (IC3T 2016). The individual papers address cutting-edge technologies and applications of soft computing, artificial intelligence and communication. In addition, a variety of further topics are discussed, which include data mining, machine intelligence, fuzzy computing, sensor networks, signal and image processing, human-computer interaction, web intelligence, etc. As such, it offers readers a valuable and unique resource.

Download or read book Leveraging Formal Concept Analysis and Pattern Mining for Moving Object Trajectory Analysis written by Feda Almuhisen and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps.

Download or read book Mining Spatiotemporal Co occurrence Patterns from Massive Data Sets with Evolving Regions written by Karthik Ganesan Pillai and published by . This book was released on 2014 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the current rates of data acquisition, the growth of data volumes in nearly all domains of our lives is reaching historic proportions [5], [6], [7]. Spatiotemporal data mining has emerged in recent decades with the main goal focused on developing data-driven mechanisms for the understanding of the spatiotemporal characteristics and patterns occurring in the massive repositories of data. This work focuses on discovering spatiotemporal co-occurrence patterns (STCOPs) from large data sets with evolving regions. Spatiotemporal co-occurrence patterns represent the subset of event types that occur together in both space and time. Major limitations of existing spatiotemporal data mining models and techniques include the following. First, they do not take into account continuously evolving spatiotemporal events that have polygon-like representations. Second, they do not investigate and provide sufficient interest measures for the STCOPs discovery purposes. Third, computationally and storage efficient algorithms to discover STCOPs are missing. These limitations of existing approaches represent important hurdles while analyzing massive spatiotemporal data sets in several application domains that generate big data, including solar physics, which is an application of our interdisciplinary research. In this work, we address these limitations by i) introducing the problem of mining STCOPs from data sets with extended (region-based) spatial representations that evolve over time, ii) developing a set of novel interest measures, and iii) providing a novel framework to model STCOPs. We also present and investigate three novel approaches to STCOPs mining. We follow this investigation by applying our algorithm to perform a novel data-driven discovery of STCOPs from solar physics data.

Download or read book Mobility Data written by Chiara Renso and published by Cambridge University Press. This book was released on 2013-10-14 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobility of people and goods is essential in the global economy. The ability to track the routes and patterns associated with this mobility offers unprecedented opportunities for developing new, smarter applications in different domains. Written by a renowned group of worldwide experts, this book surveys the myriad facets of monitoring people in motion, from spatio-temporal data modeling, to data aggregation and warehousing, to data analysis.