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13 produkter
13 produkter
Häftad, Engelska, 2019
1 622 kr
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This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Inbunden, Engelska, 2018
1 670 kr
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This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
2 049 kr
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This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Häftad, Engelska, 2006
562 kr
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Thisbookcontainsthepostworkshopproceedingsofthe7thInternationalWo- shop on Knowledge Discovery from the Web, WEBKDD 2005. The WEBKDD workshop series takes place as part of the ACM SIGKDD International Conf- ence on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto theWeb.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. In the last years, the interest for the Web as medium for communication, interaction and business has led to new challenges and to intensive, dedicated research. Many of the infancy problems in Web mining have now been solved but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges.
E-bok
PDF, Engelska, 2006708 kr
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Thisbookcontainsthepostworkshopproceedingsofthe7thInternationalWo- shop on Knowledge Discovery from the Web, WEBKDD 2005. The WEBKDD workshop series takes place as part of the ACM SIGKDD International Conf- ence on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto theWeb.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. In the last years, the interest for the Web as medium for communication, interaction and business has led to new challenges and to intensive, dedicated research. Many of the infancy problems in Web mining have now been solved but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges.
Häftad, Engelska, 2006
562 kr
Skickas inom 10-15 vardagar
TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.
E-bok
PDF, Engelska, 2006734 kr
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TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.
Häftad, Engelska, 2007
562 kr
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This book contains the postworkshop proceedings with selected revised papers from the 8th international workshop on knowledge discovery from the Web, WEBKDD 2006. The WEBKDD workshop series has taken place as part of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto the Web.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interaction and business has led to new challenges and to intensive, dedicated research.Many ofthe infancy problems in Web mining have been solvedby now, but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges. ThethemeoftheWebKDD2006workshopwas“KnowledgeDiscoveryonthe Web”, encompassing lessons learned over the past few years and new challenges for the years to come. While some of the infancy problems of Web analysis have beensolvedandproposedmethodologieshavereachedmaturity,therealityposes newchallenges:TheWebisevolvingconstantly;siteschangeanduserpreferences drift. And, most of all, a Web site is more than a see-and-click medium; it is a venue where a user interacts with a site owner or with other users, where group behavior is exhibited, communities are formed and experiences are shared.
E-bok
PDF, Engelska, 2007734 kr
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This book contains the postworkshop proceedings with selected revised papers from the 8th international workshop on knowledge discovery from the Web, WEBKDD 2006. The WEBKDD workshop series has taken place as part of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto the Web.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interaction and business has led to new challenges and to intensive, dedicated research.Many ofthe infancy problems in Web mining have been solvedby now, but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges. ThethemeoftheWebKDD2006workshopwas“KnowledgeDiscoveryonthe Web”, encompassing lessons learned over the past few years and new challenges for the years to come. While some of the infancy problems of Web analysis have beensolvedandproposedmethodologieshavereachedmaturity,therealityposes newchallenges:TheWebisevolvingconstantly;siteschangeanduserpreferences drift. And, most of all, a Web site is more than a see-and-click medium; it is a venue where a user interacts with a site owner or with other users, where group behavior is exhibited, communities are formed and experiences are shared.
562 kr
Skickas inom 5-8 vardagar
Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.
708 kr
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Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.
E-bok
PDF, Engelska, 201716 kr
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Research Paper (postgraduate) from the year 2008 in the subject Computer Science - Applied, grade: 4.0, University of Louisville (Speed College of Engineering), language: English, abstract: We give an overview of our experience in utilizing several open source packages and composing them into sophisticated applications to solve several challenging problems as part of some of the research projects at the Knowledge Discovery & Web Mining lab at the Universe of Louisville. The projects have a common theme of knowledge discovery, however their application domains span a variety of areas. These areas range from mining Web data streams to mining Astronomy related image data, as well as Web information retrieval in social multimedia websites and e-learning platforms. As is already known, a significant proportion of the effort in any real life project involving knowledge discovery in data (KDD) is devoted to the early and final stages of KDD, i.e., the data collection and preprocessing, and the visualization of the results. Given the nature of the data in our projects, we expose our experience in handling text data and image data as part of the KDD process. In addition to the open source packages that we used, we will briefly present some of the stand-alone software that we developed in the lab, in particular a suite of software for clustering and for stream data mining.
Häftad, Engelska, 2017
331 kr
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