CINQ
2004
Springer
14 years 5 months ago
2004
Springer
Algorithms for finding frequent itemsets fall into two broad classes: (1) algorithms that are based on non-trivial SQL statements to query and update a database, and (2) algorithms...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
In this paper we present the application of the inductive database approach to two practical analytical case studies: Web usage mining in Web logs and financial data. As far as co...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
Spatial data mining is a process used to discover interesting but not explicitly available, highly usable patterns embedded in both spatial and nonspatial data, which are possibly ...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
This chapter investigates subgroup discovery as a task of constraint-based mining of local patterns, aimed at describing groups of individuals with unusual distributional character...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All o...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
Mining Frequent Itemsets is the core operation of many data mining algorithms. This operation however, is very data intensive and sometimes produces a prohibitively large output. I...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
Abstract. Data mining algorithms such as the Apriori method for finding frequent sets in sparse binary data can be used for efficient computation of a large number of summaries fr...
CINQ
2004
Springer
14 years 5 months ago
2004
Springer
Many researchers in our community (this author included) regularly emphasize the role constraints play in improving performance of data-mining algorithms. This emphasis has led to ...