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

[Donald T.Mon]

I would be interested in the book for personal information, and 
perhaps for
a future graduate course in healthcare data warehousing and data 
mining.



At 07:21 PM 3/6/98 -0500, you wrote:
>
>>     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)
>>     (mobile) +44 374 478508
>>     (home)   +44 1277 362915
>>     (office) +44 1628 502583
>>     
>>     fax: +44 1628 770224
>>     
>>     website:  http://www.mcgraw-hill.co.uk
>>     
>>
>>---------------------------------------------------------------------------
>----
>>
>>***************************************************************************
> 
>>** 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
>