Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms.

Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting.

The field of Information Systems has been shifting from an ‘immersion view’, which relies on the immersion of information technology (IT) as part of the business environment, to a ‘fusion view’ in which IT is fused within the business environment, forming a unified fabric that integrates work and personal life, as well as personal and public information. In the context of this fusion view, decision support systems should achieve a total alignment with the context and the personal preferences of users. The advantage of such a view is an opportunity of seamless integration between enterprise environments and decision support system components. Thus, researchers and practitioners have to address the challenges of dealing with this shift in viewpoint and its consequences for decision making and decision support systems theories and applications. This book presents the latest innovations and advances in decision support systems with a special focus on the fusion view. These achievements will be of interest to all those involved and interested in decision making practice and research, as well as, more generally, in the fusion view of modern information systems. The book covers a wide range of topical themes including a fusion view of business intelligence and data warehousing, applications of multi-criteria decision analysis, intelligent models and technologies for decision making, knowledge management, decision support approaches and models for emergency management, and medical and other specific domains.

This book constitutes revised selected papers from the 4th International Workshop on Graphical Models for Security, GraMSec 2017, held in Santa Barbara, CA, USA, in August 2017. The 5 full and 4 short papers presented in this volume were carefully reviewed and selected from 19 submissions. The book also contains one invited paper from the WISER project. The contributions deal with the latest research and developments on graphical models for security.

There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.

"This book is the premier comprehensive reference source for the latest terms, acronyms and definitions related to all aspects of information science and technology. It provides the most current information to researchers on every level"--Provided by publisher.

During the last two decades, computer and information technologies have forced great changes in the ways businesses manage operations in meeting the desired quality of products and services, customer demands, competition, and other challenges. The Handbook of Computational Intelligence in Manufacturing and Production Management focuses on new developments in computational intelligence in areas such as forecasting, scheduling, production planning, inventory control, and aggregate planning, among others. This comprehensive collection of research provides cutting-edge knowledge on information technology developments for both researchers and professionals in fields such as operations and production management, Web engineering, artificial intelligence, and information resources management.