The smallest ideal that contains a
given concept b in G is called a principal ideal and is given by ?†“b = {c in G | c ?‰¤ b}.
Dually, ?†‘b denotes the principal filter for b. For example (see Figure 1), the principal
ideal for concept #10 contains concepts labeled by #1, #3, #5, and #10, while its
corresponding principal filter contains the set {#10, #15 , #17 , #19}.
Integrating.DM.with.DW.Techniques
Data warehousing and data mining have been greatly progressing in an independent
way. However, many researchers and practitioners recognize the need for a deep
integration of the two technologies (Han, 1998; Han & Kamber, 2000).
Figure 1. The Hasse diagram of the lattice related to context K
Toward Integrating Data Warehousing with Data Mining Techniques 26
Copyright ?© 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission
of Idea Group Inc. is prohibited.
Some studies were conducted to either adapt multidimensional data so that classical
data mining techniques can be used, or adapt data mining algorithms to the context
of multidimensional data. The first solution consists for example to first flatten
multidimensional data before applying DM techniques (Goil & Choudhary, 2001)
or to first map multidimensional data onto data sequences (Pinto et al., 2001). The
second solution aims to develop new solutions or revise existing ones to directly
mine multidimensional data as in Dong, Han, Joyce, Pei, and Wang (2001) and
Imielinski, Khachiyan, and Abdulghani (2002).
Pages:
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488