Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers
Jul 2005 · Springer
eBook
233
Halaman
reportRating dan ulasan tidak diverifikasi Pelajari Lebih Lanjut
Tentang eBook ini
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.
Beri rating eBook ini
Sampaikan pendapat Anda.
Informasi bacaan
Smartphone dan tablet
Instal aplikasi Google Play Buku untuk Android dan iPad/iPhone. Aplikasi akan disinkronkan secara otomatis dengan akun Anda dan dapat diakses secara online maupun offline di mana saja.
Laptop dan komputer
Anda dapat mendengarkan buku audio yang dibeli di Google Play menggunakan browser web komputer.
eReader dan perangkat lainnya
Untuk membaca di perangkat e-ink seperti Kobo eReaders, Anda perlu mendownload file dan mentransfernya ke perangkat Anda. Ikuti petunjuk Pusat bantuan yang mendetail untuk mentransfer file ke eReaders yang didukung.
