Data Mining For Co Location Patterns
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Data Mining for Co-location Patterns
Co-location pattern mining detects sets of features frequently located in close proximity to each other. This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc.
Preference-based Spatial Co-location Pattern Mining
The development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field. Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors’ recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns. Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.
Fuzzy Systems and Data Mining VIII
Fuzzy logic is vital to applications in the electrical, industrial, chemical and engineering realms, as well as in areas of management and environmental issues. Data mining is indispensible in dealing with big data, massive data, and scalable, parallel and distributed algorithms. This book presents papers from FSDM 2022, the 8th International Conference on Fuzzy Systems and Data Mining. The conference, originally scheduled to take place in Xiamen, China, was held fully online from 4 to 7 November 2022, due to ongoing restrictions connected with the COVID-19 pandemic. This year, FSDM received 196 submissions, of which 47 papers were ultimately selected for presentation and publication after a thorough review process, taking into account novelty, and the breadth and depth of research themes falling under the scope of FSDM. This resulted in an acceptance rate of 23.97%. Topics covered include fuzzy theory, algorithms and systems, fuzzy applications, data mining and the interdisciplinary field of fuzzy logic and data mining. Offering an overview of current research and developments in fuzzy logic and data mining, the book will be of interest to all those working in the field of data science.