By Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar Bartenstein
This e-book constitutes the completely refereed joint post-proceedings of the fifteenth overseas convention on purposes of Declarative Programming and data administration, INAP 2004, and the 18th Workshop on common sense Programming, WLP 2004, held together in Potsdam, Germany in March 2004.
The 18 revised complete papers provided including an invited instructional lecture and an invited paper have been chosen in the course of rounds of reviewing and development. The papers are prepared in topical sections on wisdom administration and choice aid, constraint programming and constraint fixing, and declarative programming and Web-based systems.
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Extra info for Applications of Declarative Programming and Knowledge Management: 15th International Conference on Applications of Declarative Programming and Knowledge
The average size of the maximal potentially frequent itemsets is 5 and 10 respectively . The last one consists of 100 thousand transactions with an average 25 number of items per transaction and the average length of potentially frequent patterns is 20. There exist exponentially numerous frequent itemsets in this data set when the support threshold goes down. There are pretty long frequent itemsets as well 42 X. -U. Sattler, and I. Geist as a large number of short frequent itemsets in it. It is a relatively dense dataset and contains mixtures of short and long frequent itemsets.
Path curcnt = ik . count + 1; update the table F P ; else insert ik into the table F P ; curpath += ik ; Fig. 3. Algorithm for constructing table F P 38 X. -U. Sattler, and I. Geist 2. Construct the table F P . Frequent items in T are sorted in descending order by frequency. ) Each frequent item is tested as follows. – If the item does not have the same item and path as those in the F P , insert it into the F P as a new item with the count being 1. – Otherwise, update the F P by increasing the count by 1.
C Springer-Verlag Berlin Heidelberg 2005 SQL Based Frequent Pattern Mining with FP-Growth 33 uses only two full I/O scans of the database, and avoids iterative candidate generation. The integration of data mining with database systems is an emergent trend in database research and development area. This is particularly driven by the following reasons. – Explosion of the data amount stored in databases such as Data Warehouses. – Database systems provide powerful mechanisms for accessing, ﬁltering, indexing data.