Computational Intelligence in Data Mining - Volume 3: Proceedings of the International Conference on CIDM, 20-21 December 2014
10
Citation
0
Reference
20
Related Paper
Citation Trend
Abstract:
The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.Keywords:
Business Intelligence
Intelligence analysis
Cite
Commercial applications of data mining in areas such as e-commerce, market-basket analysis, text-mining, and web-mining have taken on a central focus in the JCDD community. However, there is a significant amount of innovative data mining work taking place in the context of scientific and engineering applications that is not well represented in the mainstream KDD conferences. For example, scientific data mining techniques are being developed and applied to diverse fields such as remote sensing, physics, chemistry, biology, astronomy, structural mechanics, computational fluid dynamics etc. In these areas, data mining frequently complements and enhances existing analysis methods based on statistics, exploratory data analysis, and domain-specific approaches. On the surface, it may appear that data from one scientific field, say genomics, is very different from another field, such as physics. However, despite their diversity, there is much that is common across the mining of scientific and engineering data. For example, techniques used to identify objects in images are very similar, regardless of whether the images came from a remote sensing application, a physics experiment, an astronomy observation, or a medical study. Further, with data mining being applied to new types of data, such as mesh data from scientific simulations, there is the opportunity to apply and extend data mining to new scientific domains. This one-day workshop brings together data miners analyzing science data and scientists from diverse fields to share their experiences, learn how techniques developed in one field can be applied in another, and better understand some of the newer techniques being developed in the KDD community. This is the fourth workshop on the topic of Mining Scientific Data sets; for information on earlier workshops, see http://www.ahpcrc.org/conferences/. This workshop continues the tradition of addressing challenging problems in a field where the diversity of applications is matched only by the opportunities that await a practitioner.
Exploratory data analysis
Scientific field
Cite
Citations (0)
Natural science
Cite
Citations (7)
This paper gives a good overview of Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become an essential component in various fields of human life. Advancements in Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities have evolved the present day’s data mining applications and these applications have enriched the various fields of human life including business, education, medical, scientific etc. Hence, this paper discusses the various improvements in the field of data mining from past to the present and explores the future trends
Business Intelligence
Human life
Cite
Citations (1)
Intelligent Data Mining Techniques and Applications is an organized edited collection of contributed chapters covering basic knowledge for intelligent systems and data mining, applications in economic and management, industrial engineering and other related industrial applications. The main objective of this book is to gather a number of peer-reviewed high quality contributions in the relevant topic areas. The focus is especially on those chapters that provide theoretical/analytical solutions to the problems of real interest in intelligent techniques possibly combined with other traditional tools, for data mining and the corresponding applications to engineers and managers of different industrial sectors. Academic and applied researchers and research students working on data mining can also directly benefit from this book.
Cite
Citations (54)
Data mining is an interdisciplinary area of computer science combining database systems, statistical and machine learning approaches, and artificial intelligence focused on extraction of patterns and implicit relationships from data. In the era of high-throughput -omics technologies, the amount of scientific data that needs to be analyzed becomes problematic if not supported by powerful computers and sophisticated data mining algorithms, and thus, data mining techniques become increasingly popular among the scientific community. This chapter describes in detail the data mining process with special emphasis on its application in the field of -omics research.
Scientific discovery
Cite
Citations (12)
Discovery and Data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. Knowledge Discovery and Data Mining: Challenges and Realities is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This premier reference source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.
Data discovery
Data Sharing
Cite
Citations (116)
A lot of research and analysis has been done that focuses on the implementation, use, and evaluation of artificial intelligence techniques. The analysis is done on different techniques and variations of known methods regarding their characteristics like speed, performance, and effectiveness using scientific methods, statistics and mathematical proofs. On the other end of the spectrum, a lot of research has been done on high-level data mining as well. The research on data mining usually stops at technical implementations and focuses mainly on high-level techniques to manipulate the bulk of data to be mined. The physical implementation is usually abstracted and left for libraries to optimize. In order to use this research in the area of big data, the areas of AI and Data mining need to be conjoined so that the appropriate knowledge from both technical and conceptual areas is used. The purpose of this literature review is to systematically review the research done on both the technical and conceptual ends of the spectrum and to find the overlapping techniques. This is needed to get a clear understanding of the entire knowledge extraction process from big data to business value. The research results in a broad view of all techniques and their appropriateness towards big data. In order to make decisions on the techniques used for a specific data mining problem, a broad view of all available solutions is needed. This paper attempts to deliver it by investigating all possibilities and discuss their advantages and disadvantages relating to big data.
Implementation
Cite
Citations (3)
The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.
K-optimal pattern discovery
Cite
Citations (13)
Data mining is the process of finding the patterns, associations or relationships among data using different analytical techniques involving the creation of a model and the concluded result will become useful information or knowledge. The advancement of the new medical deceives and the database management systems create a huge number of data-bases in the biomedicine world. Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. This paper introduces some basic data mining techniques, unsupervised learning and supervising learning, and reviews the application of data mining in biomedicine. Applications of the multimedia mining, including text, image, video and web mining are discussed. The key issues faced by the computing professional, medical doctors and clinicians are highlighted. We also state some foreseeable future developments in the field. Although extracting useful information from raw biomedical data is a challenging task, data mining is still a good area of scientific study and remains a promising and rich field for research.
Biomedicine
Cite
Citations (26)
Data mining, which is also known as knowledge discovery, is one of the most popular topics in information technology. It concerns the process of automatically extracting useful information and has the promise of discovering hidden relationships that exist in large databases. These relationships represent valuable knowledge that is crucial for many applications. This paper presents a review of works on current applications of data mining, which focus on four main application areas, including bioinformatics data, information retrieval, adaptive hypermedia and electronic commerce. How data mining can enhance functions for these four areas is described. The reader of this paper is expected to get an overview of the state-of-the-art research associated with these applications. Furthermore, we identify the limitations of current works and raise several directions for future research.
Cite
Citations (50)