DM: Fwd: Re[2]: Proposed book on Data Mining

[Dorothy Firsching]

>     D.J.Hatter
>     Publisher, Computing and Information Systems
>     McGraw-Hill Publishing Company, 
>     Shoppenhangers Rd, Maidenhead, Berkshire, England SL6 2QL
>     
>     Email: dave_hatter@mcgraw-hill.com
>     
>     Phone: (In order of probability)
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> 
>** Your views are requested on a proposed new publication in Data 
>Mining ** 
>***************************************************************************
>At McGraw-Hill we have a proposal from Sarab Anand of Ulster 
>University on
the 
>subject which is being considering for publication.  The proposal, 
>which is 
>summarised below, has been reviewed and has received praise for its
technical 
>and academic fidelity; we now need to assess the interest in the book
among the 
>informed community.  What I would like to ask, therefore, is whether 
>you 
>would be interested in the book, for your own use or as a text for
students. A 
>brief e-note indicating your view, together with any observation 
>which
occurs to
>you would help me greatly. An indication of the extent to which the 
>subject 
>appears in advanced u/g and p/g courses would be particularly 
>useful. In the 
>event of there being support for its publication we would be pleased 
>to
make it 
>available at a preferred price for members of this group.
>
>Thank you very much for your help. It is our view at McGraw-Hill 
>that the
book 
>promises to be a significant addition to the literature and your 
>response
will 
>assist us in our decision on whether to publish. Please address your
response to
>me, dave_hatter@mcgraw-hill.com
>
>1: Introduction; Anand, Buchner, Hughes
>Overview of Data Mining technologies. What Data Mining is and why it 
>is
needed. 
>PART I:   Data Pre-Processing
>2: Dealing with Missing Data; Ken Totton, Gavin Meggs, Blaise Egan 
>(BT) Most 
>common attribute value to bayesian and statistical models.
>3: Data Dimensionality Reduction; Ron Kohavi(Stanford), 
>McClean,Scotney
(Ulster)
>Covers techniques  to reduce the dimensionality of the data. 
>4: Noise Modelling; Ray Hickey (Ulster)
>"How can a discovery algorithm cope with inaccurate data" 
>PART II Discovery Methodologies; Machine Learning Based Techniques
>5: Rule Induction / Information Theory: Padhraic Smyth (U of 
>California,
Irvine)

>The use of Information Theoretic measures within rule discovery is 
>studied. 
>6: Conceptual Clustering; A Doug Talbert, Doug Fisher, Vandebilt U,
Tennessee 
>Discusses problems in present clustering techniques & presents novel
solutions. 
>7: Heuristic Techniques; V. Rayward-Smith (University of East Anglia)
Techniques
>such as Simulated Annealing, Genetic Algorithms & hybrid techniques. 
>8: 
>Connectionism and Data Mining; Liu, Setiono (National U of Singapore)
>This chapter discusses techniques available for rule extraction. 
>Uncertainty 
>Based Techniques:
>9: Rough Set Analysis; Ivo Duntsch (Ulster), Gunther Gediga 
>(Onsabruck,
Germany)
>Basic concepts &  two techniques for obtaining a logic of rough sets
>10: Bayesian Belief Networks and L-L Modelling; Shapcott, Bell,  Liu
(Ulster) 
>Basic concepts of l-l models for two variables & their 
>generalisation.
Database 
>Support for Data Mining:
>11: Database Support for Attribute Oriented Induction;J.Han (Simon 
>Fraser U) 
>Attribute Oriented Induction operations mapped onto database 
>operations.
>12: Discovery in Distributed and Heterogeneous 
>Databases;Bell,Anand,Hua
(Ulster)
>Initial work on requirements for distributed database support for 
>discovery.
>13: Distributed Statistical Databases; McClean, Scotney  (Ulster) The
structure 
>of a micro/macro data model and relations is examined. PART III The 
>Role
of the 
>Human:
>14: Using Background Knowledge; A. Tuzhilin (New York University) 
>Covers the 
>role of domain knowledge within Data Mining. 
>PART IV Knowledge Post-Processing
>15: Knowledge Filtering; Friedrich Gebhardt (GMD Labs, Germany )
>Covers both aspects of interestingness discussing its different 
>facets and 
>providing a survey of measures used to address each of these facets.
Covers both
>objective as well as subjective measures.
>16: Knowledge Validation;  Ken Totton, Gavin Meggs, Blaise Egan BT 
>Labs,
England
>A number of different approaches to knowledge validation are 
>reviewed. 
> 
Dorothy Firsching
CEO
Nautilus Systems, Inc.
3867 Alder Woods Court
Fairfax, VA  22033
http://www.nautilus-systems.com/
nautilus-info@nautilus-systems.com