Knowledge Discovery in Spatial Data

1. Front Matter

   Pages i-xxix

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2. Introduction

   • Yee Leung

   Pages 1-12

3. Discovery of Intrinsic Clustering in Spatial Data

   • Yee Leung

   Pages 13-96

4. Statistical Approach to the Identification of Separation Surface for Spatial Data

   • Yee Leung

   Pages 97-142

5. Algorithmic Approach to the Identification of Classification Rules or Separation Surface for Spatial Data

   • Yee Leung

   Pages 143-221

6. Discovery of Spatial Relationships in Spatial Data

   • Yee Leung

   Pages 223-276

7. Discovery of Structures and Processes in Temporal Data

   • Yee Leung

   Pages 277-319

8. Summary and Outlooks

   • Yee Leung

   Pages 321-327

9. Back Matter

   Pages 329-360

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When I ?rst came across the term data mining and knowledge discovery in databases, I was excited and curious to ?nd out what it was all about. I was excited because the term tends to convey a new ?eld that is in the making. I was curious because I wondered what it was doing that the other ?elds of research, such as statistics and the broad ?eld of arti?cial intelligence, were not doing. After reading up on the literature, I have come to realize that it is not much different from conventional data analysis. The commonly used de?nition of knowledge discovery in databases: “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” is actually in line with the core mission of conventional data analysis. The process employed by conventional data analysis is by no means trivial, and the patterns in data to be unraveled have, of course, to be valid, novel, useful and understandable. Therefore, what is the commotion all about? Careful scrutiny of the main lines of research in data mining and knowledge discovery again told me that they are not much different from that of conventional data analysis. Putting aside data warehousing and database m- agement aspects, again a main area of research in conventional database research, the rest of the tasks in data mining are largely the main concerns of conventional data analysis.

• Algorithm
• Clustering
• Geographical Information System
• Knowledge Discovery
• Remote Sensing
• Spatial Data Mining
• classification
• data mining
• geographic data

From the reviews:

“A research monograph on methods and algorithms, which represents the author’s rich research experience and achievements. Such perspective provides an invaluable resource for advanced users. … it achieves its aim of providing thoughtful and provocative demonstrations on the issues of spatial knowledge discovery and data mining from the conceptual, theoretical and empirical points of view. … recommended for scholars in any discipline interested in the geographical dimensions of large data sets. … an up-to-date contribution to the field of spatial knowledge discovery and data mining.” (Xinyue Ye, Regional Studies, Vol. 45 (6), June, 2011)

• Dept. Geography &, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong/PR China

  Yee Leung

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