Fundamentals of database systems (2nd ed.)
Fundamentals of database systems (2nd ed.)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Data mining
Machine Learning - special issue on inductive logic programming
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization techniques for queries with expensive methods
ACM Transactions on Database Systems (TODS)
Rule Discovery in Telecommunication AlarmData
Journal of Network and Systems Management
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Improving the Discovery of Association Rules with Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Text mining documents in electronic data interchange environment
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Using text mining techniques in electronic data interchange environment
WSEAS Transactions on Computers
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One of the most challenging problems in data manipulation in the future is to be able to efficiently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion functional dependencies. Our view of a possible approach for this task is to specify and query i-extended databases, which are databases that in addition to data also contain exceedingly defined generalizations about the data. I-extended database is a database that has similar properties then an inductive database [6] in certain scene and it could be also an alternative to inductive database. I formalize this concept and show how it can be use throughout the whole process of DM due to the closure property of the framework. Suppose that a simple query language can be defined using normal database terminology. I also demonstrate the use of this framework to model typical DM processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes. The main aim of implementing i-extended database is to be interacted by a spatial Data Mining query called Knowledge Discovery Query Language (KDQL) described in [21]. The KDQL was demonstrated and introduced as a query in the ODBC_KDD (2) model in [22].