Item-level analysis allows for the examination of qualitative age and individual differences in skill acquisition, which are obscured when aggregating data across items. In the present study, item-level strategy shifts were generally gradual and variable, rather than abrupt and collective. Strategy shift reversions were frequent, and the total transition space was extensive, for both younger an...

Finding a comprehensive set of patterns that truly captures the characteristics of a database is a complicated matter. Frequent item set mining attempts this, but low support levels often result in exorbitant amounts of item sets. Recently we showed that by using MDL we are able to select a small number of item sets that compress the data well [15]. Here we show that this small set is a good ap...

While in standard frequent item set mining one tries to find item sets the support of which exceeds a user-specified threshold (minimum support) in a database of transactions, we strive to find item sets for which the similarity of their covers (that is, the sets of transactions containing them) exceeds a user-specified threshold. Starting from the generalized Jaccard index we extend our approa...

One of the major problems in frequent pattern mining is the explosion of the number of results, making it difficult to identify the interesting frequent patterns. In a recent paper [14] we have shown that an MDL-based approach gives a dramatic reduction of the number of frequent item sets to consider. Here we show that MDL gives similarly good reductions for frequent patterns on other types of ...

Knowledge exploration from the large set of data, generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how t...

Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we present a more efficient algorithm for mining complete sets of frequent item sets. In designing ou...

For large scale distributed systems, designing energy efficient protocols and services has become as significant as considering conventional performance criteria like scalability, reliability, fault-tolerance and security. We consider frequent item set discovery problem in this context. Although it has attracted attention due to its extensive applicability in diverse areas, there is no prior wo...

In this paper, we present a polynomial-time algorithm for TPQ (tree pattern queries) minimization without XML constraints involved. The main idea of the algorithm is a dynamic programming strategy to find all the matching subtrees within a TPQ. A matching subtree implies a redundancy and should be removed in such a way that the semantics of the original TPQ is not damaged. Our algorithm consist...