Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views

Abstract. Data mining is an iterative process. Users issue series of similar data mining queries, in each consecutive run slightly modifying either the definition of the mined dataset, or the parameters of the mining algorithm. This model of processing is most suitable for incremental mining algorithms that reuse the results of previous queries when answering a given query. Incremental mining algorithms require the results of previous queries to be available. One way to preserve those results is to use materialized data mining views. Materialized data mining views store the mined patterns and refresh them as the underlying data change. Data mining and knowledge discovery often take place in a data warehouse environment. There can be many relatively small materialized data mining views defined over the data warehouse. Separate refresh of each materialized view can be expensive, if the refresh process has to re-discover patterns in the original database. In this paper we present a nove...

• Type: Conference
• Year: 2005
• Where: DAWAK
• Authors: Mikolaj Morzy, Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz
• Keywords: Data Mining, Data Mining Views, DAWAK 2005, Information Management, Mining Algorithms