Titlebook: Knowledge Discovery in Databases: PKDD 2004; 8th European Confere Jean-Fran?ois Boulicaut,Floriana Esposito,Dino Ped Conference proceedings

BILL

myopia

Real-World Learning with Markov Logic Networkselp from people. This requires automating the data cleaning and integration process, handling multiple types of objects and relations at once, and easily incorporating domain knowledge. In this talk, I describe how we are pursuing these aims using Markov logic networks, a representation that combine

CLOWN

Strength in Diversity: The Advance of Data Analysiss. As a consequence of the development of the computer, things have changed dramatically and now there are several such disciplines, including machine learning, pattern recognition, and data mining. This paper looks at some of the similarities and some of the differences between these disciplines, n

笨拙的我

神化怪物

善于騙人

BOON

Combining Multiple Clustering Systemssters of different systems. In this work, the clusters of individual systems are represented in a common space and their correspondence estimated by either “clustering clusters” or with Singular Value Decomposition. The approaches are evaluated for the task of topic discovery on three major corpora

ear-canal

殺子女者

A Framework for Data Mining Pattern Managementan be extracted from raw data by using data mining tools (a-posteriori patterns) but also defined by the users and used for example to check how well they represent some input data source (a-priori patterns); (iii) since source data change frequently, issues concerning pattern validity and synchroni

RADE