CINQ
2004
Springer
14 years 25 days 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 25 days 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 25 days 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 25 days 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 25 days 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 25 days 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 25 days 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 25 days 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 ...