Data Mining: Theory, Methodology, Techniques, and Applications (Lecture Notes in Computer Science, 3755) 2006th Edition by Graham J. Williams (PDF)

Description

This volume provides a snapshot of the current state of the art in data mining, presenting it both in terms of technical developments and industrial applications. The collection of chapters is based on works presented at the Australasian Data Mining conferences and industrial forums. Authors include some of Australia’s leading researchers and practitioners in data mining. The volume also contains chapters by regional and international authors.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐As an Australian, I was pleased to see several papers in this collection from researchers based in Australia, using data collected there. This was due to the editors being from Australia and a longstanding tradition of extensive data mining research at the local universities. Though I should add that like most of the Springer books of conference proceedings, the overall list of contributors is very international.One chapter, “An Application of Time-Changing Feature Selection” looked at extracting knowledge from health records. Its figures 2 and 3 plotted item frequencies against different time changing features, or using the same time changing qualitative feature. Both figures have a curious pattern of curves clearly discernible in the display of points in the figures. The accompanying text does not appear to go into why the curves exist. Is it possibly an artifact of mathematics of discretising some equations to take on only integer values? Or is there something else. In either case, the text should perhaps have elaborated the discussion.Several other chapters talk about parsing web pages and extracting tokens that characterise a topic. One such chapter, ‘Informing the Curious Negotiator’, comes with useful pseudocode about how to programmatically isolate text from web pages from news sites, that is actually news information. The problem is that such pages often might have ancillary text that are ads, for example, or boilerplate text about the newspaper company. The chapter points out that a comparative analysis of pages from the website can sometimes or often identify constant text across different pages. This text can be indicative of a template used to generate the web pages, and thus discarded. The overall idea is to take advantage of the fact that many news sites now have very structured pages, based on only a few common templates per website. You can use this chapter to bootstrap writing your own code for news isolation and analysis.Another chapter was on Scamseek. An Australian effort to algorithmically download and analyse financial pages, to find those that might be fradulant. The chapter has only 1 reference, because of confidentiality agreements with the Australian federal government that provided funding and presumably some of the data. Aside from the lack of references, the chapter is frustratingly sparse about the precise lower level details of implementation. While understandable, the reader may not be able to easily build on this to make her own antiscam project.

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