By Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar Bartenstein
This ebook constitutes the completely refereed joint post-proceedings of the fifteenth foreign 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 educational lecture and an invited paper have been chosen in the course of rounds of reviewing and development. The papers are geared up in topical sections on wisdom administration and selection aid, constraint programming and constraint fixing, and declarative programming and Web-based systems.
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So we further use subquery instead of temporary tables. The data table T is scanned in the (id, item) order and combined with frequent itemsets table F to remove all infrequent items and sort in support descending order as F , and then passed to the user deﬁned function Path, which collects all the preﬁxes of items in a transaction. 1 Performance Evaluation Dataset We use synthetic transaction data generation with program describe in Apriori algorithm paper  for experiment. The nomenclature of these data sets is of the form TxxIyyDzzzK, where xx denotes the average number of items present per transaction, yy denotes the average support of each item in the data set and zzzK denotes the total number of transactions in K (1000’s).
If an item does not exist in the table F P or there exist the same items as this item in the table F P but their corresponding path are diﬀerent, insert the item into table F P . Otherwise, update the table F P by incrementing the item’s count by 1. In the process of mining frequent patterns using F P , we need to recursively construct conditional frequent pattern table, named ConF P , for each frequent item. Due to the path column of F P storing the set of preﬁx items of each frequent item, it’s easy to ﬁnd the items which co-occur with this item by deriving all its path in the table FP.
They have also compared the performance of the SQL-92 architecture with SQL-OR based architecture, and they are on the base of Apriori-like approach. 6 Summary and Conclusion We have implemented SQL based frequent pattern mining using F P -growthlike approach. We represent F P -tree using a relational table F P and proposed a method to construct this table. To improve its performance, a table called EF P is introduced, which is in fact stores all information of frequent item sets and their preﬁx path of each transaction.