Most works on norms have investigated how norms are regulated using institutional mechanisms. Very few works have focused on how an agent may infer the norms of a society without the norm being explicitly given to the agent. This paper describes how an agent can make use of the proposed norm identification architecture to identify norms. This paper explains how an agent using this architecture ...

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may impel users to provide spurious information. In this chapter, we study whether users can be encouraged to provide correct information by ensuring that the mining process cannot, with any reasonable degree of certainty, violate their privacy. Our analysis is in the context of extracting ...

Over the years, data mining has attracted most of the attention from the research community. The researchers attempt to develop faster, more scalable algorithms to navigate over the ever increasing volumes of spatial gene expression data in search of meaningful patterns. Association rules are a data mining technique that tries to identify intrinsic patterns in spatial gene expression data. It h...

Efficient discover of association rules in large databases is a we 1 studied problem and several ap1y proaches have been proposed. However, it is non trivial to maintain the association rules current when the database is updated since, such updates could invalidate existing rules or introduce new rules. In this paper, we propose an incremental updating technique btied on tie ittive borders, for...

In this paper, we survey efforts devoted to discovering interesting exceptions from data in data mining. An exception differs from the rest of data and thus is interesting and can be a clue for further discoveries. We classify methods into exception instance discovery, exception rule discovery, and exception structured-rules discovery and give a condensed and comprehensive introduction.

This paper presents an efficient One Pass Association Mining technique i.e. OPAM, which finds all the frequent itemsets without generating any candidate sets. OPAM is basically an integration of two techniques: a correlogram matrix based technique to generate all the frequent 1and 2-itemset in a single scan over the database and a technique that uses vertical layout concept to generate the rest...

Mining association rules from large databases of business data is an important topic in data mining. In many applications, there are explicit or implicit taxonomies (hierarchies) for items, so it may be useful to find associations at levels of the taxonomy other than the primitive concept level. Previous work on the mining of generalized association rules, however, assumed that the taxonomy of ...

In this paper, the mining of single-dimensional association rule and non-repetitive predicate multi-dimensional association rule were integrated. We proposed an algorithm for mining of hybrid-dimensional association rule using a data cube structure. Preliminary result shows that when the number of tuples is large, the algorithm is efficient.

In this paper, we first focus our attention on the question of how much space remains for performance improvement over current association rule mining algorithms. Our strategy is to compare their performance against an “Oracle algorithm” that knows in advance the identities of all frequent itemsets in the database and only needs to gather their actual supports to complete the mining process. Ou...

Supervised descriptive rule discovery techniques like subgroup discovery are quite popular in applications like fraud detection or clinical studies. Compared with other descriptive techniques, like classical support/confidence association rules, subgroup discovery has the advantage that it comes up with only the top-k patterns, and that it makes use of a quality function that avoids patterns un...