The PASCAL Visual Object Classes Challenge

An ontology is an explicit formal conceptualization of some domain of interest Ontologies are increasingly used in various fields such as knowledge management, information extraction, and the semantic web Ontology evaluation is the problem of assessing a given ontology from the point of view of a particular criterion of application, typically in order to determine which of several ontologies would best suit a particular purpose This paper presents a survey of the state of the art in ontology evaluation

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A survey of ontology evaluation techniques

Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.

A survey of clustering ensemble algorithms

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

Wed, 05 Dec 2018 22:36:00 GMT forecasting methods and applications 3rd pdf PDF | On Jan 1, 1984, S ~G Makridakis and others published Forecasting: Methods and Applications Tue, 04 Dec 2018 23:06:00 GMT (PDF) Forecasting: Methods and Applications ResearchGate Forecasting: methods and applications. This book was published in 1998, and for nearly 20 years I maintained an associated website at this address. Fri, 30 Nov 2018 14:35:00 GMT Forecasting: methods and applications | Rob J Hyndman Prod 2100-2110 Forecasting Methods 2 1. Framework of planning decisions Let us first remember where the inventory control decisions may take place. Fri, 07 Dec 2018 14:13:00 GMT Forecasting Methods UCLouvain 2002 Forecasting: Methods and Applications Makridakis, ... this 3rd edition very wisely includes some more advanced forecasting methods such as dynamic regression, ... Sat, 01 Dec 2018 22:41:00 GMT 2002 Forecasting: Methods and Applications HEPHAESTUS Methods and Applications Third Edition Spyros Makridakis European Institute of Business ... major forecasting methods 516 The use of different forecasting Tue, 04 Dec 2018 22:37:00 GMT Methods and Applications Max Planck Society MATH6011: Forecasting â€œAll models are wrong, ... S.C. and Hyndman, R.J. 1998, Forecasting: Methods and Applications 3rd Ed., New York: Wiley as text book. Wed, 21 Nov 2018 17:31:00 GMT MATH6011: Forecasting University of Southampton Save As PDF Ebook forecasting methods and applications ... FOUR LAMAS OF DOLPO AUTOBIOGRAPHIES OF FOUR TIBETAN LAMAS INTRODUCTION AND TRANSLATIONS VOL I 3RD [PDF] Tue, 04 Dec 2018 19:10:00 GMT forecasting methods and applications makridakis pdf ... forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online books in PDF, EPUB, Tuebl, and Mobi Format. Thu, 06 Dec 2018 07:26:00 GMT forecasting methods and applications 3rd ed | Download ... INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 : ... Some applications of forecasting ... Qualitative techniques in forecasting Time series methods Mon, 19 Nov 2018 11:49:00 GMT INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 ... 3 Hierarchical forecasting 9 3 Advanced methods 9. Forecasting: principles and practice 7 Assumptions â€¢ This is not an introduction to R. I assume you are broadly ... Thu, 06 Dec 2018 22:49:00 GMT Forecasting: Principles & Practice, Rob J Hyndman, 2014 forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online here in PDF or EPUB. Please click button to get ... Mon, 03 Dec 2018 08:27:00 GMT Forecasting Methods And Applications 3rd Ed | Download ... Forecasting methods can be classified as qualitative or quantitative. ... practical applications. 15-4 Chapter 15 Time Series Analysis and Forecasting Fri, 07 Dec 2018 12:33:00 GMT PDF Time Series Analysis and Forecasting Cengage FORECASTING METHODS AND APPLICATIONS 3RD EDITION PDF READ Forecasting Methods And Applications 3rd Edition pdf. Download Forecasting Methods And Applications 3rd ... Sun, 11 Nov 2018 17:14:00 GMT Free Forecasting Methods And Applications 3rd Edition PDF Forecasting Methods and Applications. 3rd ed. New York: John Wiley & Sons, 1998. Sat, 08 Dec 2018 09:40:00 GMT Forecasting Methods and Applications Book Harvard ... Preface In preparing the manuscript for the third edition of Forecasting: methods and applications, one of our primary goals has been to make the book as complete and ... Wed, 05 Dec 2018

Forecasting methods and applications.

In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two documents use different collections of core words to represent the same topic, they may be falsely assigned to different clusters due to the lack of shared core words, although the core words they use are probably synonyms or semantically associated in other forms. The most common way to solve this problem is to enrich document representation with the background knowledge in an ontology. There are two major issues for this approach: (1) the coverage of the ontology is limited, even for WordNet or Mesh, (2) using ontology terms as replacement or additional features may cause information loss, or introduce noise. In this paper, we present a novel text clustering method to address these two issues by enriching document representation with Wikipedia concept and category information. We develop two approaches, exact match and relatedness-match, to map text documents to Wikipedia concepts, and further to Wikipedia categories. Then the text documents are clustered based on a similarity metric which combines document content information, concept information as well as category information. The experimental results using the proposed clustering framework on three datasets (20-newsgroup, TDT2, and LA Times) show that clustering performance improves significantly by enriching document representation with Wikipedia concepts and categories.

Exploiting Wikipedia as external knowledge for document clustering

The goal of the next generation Web is to build virtual communities, wherein software agents and people can work in cooperation by sharing knowledge. To achieve this goal, the emerging Semantic Web community has proposed ontologies to express knowledge in a machine understandable way. The process of building and maintaining ontologies, which is known as Ontology Engineering, presents unique challenges. These challenges are related to lack of trustworthy and authoritative knowledge sources and absence of a centralized repository to locate ontologies to be reused. In this paper, we propose a Semantic Web portal, called OntoKhoj that is designed to simplify the Ontology Engineering process. The methodology in developing OntoKhoj is based on algorithms used for searching, aggregating, ranking and classifying ontologies in Semantic Web. The proposed OntoKhoj would 1) allow agents and ontology engineers to retrieve trustworthy, authoritative knowledge, and 2) expedite the process of ontology engineering through extensive reuse of ontologies. We have implemented the OntoKhoj portal and further validated our system on the real ontological data in the Semantic Web.

OntoKhoj: a semantic web portal for ontology searching, ranking and classification

Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified-Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.

Flexible Frameworks for Actionable Knowledge Discovery

Assessing network vulnerability before potential disruptive events such as natural disasters or malicious attacks is vital for network planning and risk management It enables us to seek and safeguard against most destructive scenarios in which the overall network connectivity falls dramatically Existing vulnerability assessments mainly focus on investigating the inhomogeneous properties of graph elements, node degree for example, however, these measures and the corresponding heuristic solutions can provide neither an accurate evaluation over general network topologies, nor performance guarantees to large scale networks To this end, in this paper, we investigate a measure called pairwise connectivity and formulate this vulnerability assessment problem as a new graph-theoretical optimization problem called &#946;-disruptor, which aims to discover the set of critical node/edges, whose removal results in the maximum decline of the global pairwise connectivity Our results consist of the NP-Completeness and inapproximability proof of this problem, an O(log n loglog n) pseudo-approximation algorithm for detecting the set of critical nodes and an O(log^15 n) pseudo-approximation algorithm for detecting the set of critical edges In addition, we devise an efficient heuristic algorithm and validate the performance of the our model and algorithms through extensive simulations

On Approximation of New Optimization Methods for Assessing Network Vulnerability

With the explosive growth of the size of databases, many knowledge discovery applications deal with large quantities of data. There is an urgent need to develop methodologies which will allow the applications to focus search to a potentially interesting and relevant portion of the data, which can reduce the computational complexity of the knowledge discovery process and improve the interestingness of discovered knowledge. Previous work on semantic query optimization, which is an approach to take advantage of domain knowledge for query optimization, has demonstrated that significant cost reduction can be achieved by reformulating a query into a less expensive yet equivalent query which produces the same answer as the original one. In this paper, we introduce a method to utilize three types of domain knowledge in reducing the cost of finding a potentially interesting and relevant portion of the data while improving the quality of discovered knowledge. In addition, we propose a method to select relevant domain knowledge without an exhaustive search of all domain knowledge. The contribution of this paper is that we lay out a general framework for using domain knowledge in the knowledge discovery process effectively by providing guidelines.

Eun Kyo Park

Papers

Exploiting Wikipedia as external knowledge for document clustering

OntoKhoj: a semantic web portal for ontology searching, ranking and classification

Flexible Frameworks for Actionable Knowledge Discovery

On Approximation of New Optimization Methods for Assessing Network Vulnerability

Using domain knowledge in knowledge discovery