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Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in a given partial order. We consider the problem of discovering frequently occurring episodes in a sequence. Once such episodes are known, one can produce rules for(More)
A general framework for minimisation-based belief change is presented. A problem instance is made up of an undirected graph, where a formula is associated with each vertex. For example, vertices may represent spatial locations, points in time, or some other notion of locality. Information is shared between vertices via a process of minimisation over the(More)
One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for finding all such descriptions. We give bounds for the(More)
Discovery of association rules .is an important database mining problem. Current algorithms for finding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very significant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a(More)
Sequences of events describing the behavior and actions of users or systems can be collected in several domains. In this paper we consider the problem of recognizing frequent episodes in such sequences of events. An episode is deened to be a collection of events that occur within time intervals of a given size in a given partial order. Once such episodes(More)
The discovery of functional dependencies from relations is an important database analysis technique. We present TANE, an efficient algorithm for finding functional dependencies from large databases. TANE is based on partitioning the set of rows with respect to their attribute values, which makes testing the validity of functional dependencies fast even for(More)
Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of items that are found in a sufficient number of examples. The fundamental task of association rule and frequent set discovery has been extended(More)
Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form &#8220;for 90% of the rows of the relation, if the row has value 1 in the columns in set <italic>W</italic>, then it has 1 also in column B&#8221;. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules(More)