Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers
2005년 7월 · Springer
eBook
233
페이지
report검증되지 않은 평점과 리뷰입니다. 자세히 알아보기
eBook 정보
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.
이 eBook 평가
의견을 알려주세요.
읽기 정보
스마트폰 및 태블릿
노트북 및 컴퓨터
컴퓨터의 웹브라우저를 사용하여 Google Play에서 구매한 오디오북을 들을 수 있습니다.
eReader 및 기타 기기
Kobo eReader 등의 eBook 리더기에서 읽으려면 파일을 다운로드하여 기기로 전송해야 합니다. 지원되는 eBook 리더기로 파일을 전송하려면 고객센터에서 자세한 안내를 따르세요.
