in this paper are present the results of measurements of magnetic induction at some locationsin city bor (the east serbia) dating from the household electric devices and transformer stations suited near the residental areas. stress is on the extremely low frequent electromagnetic radiation from transformer stations and other high voltage instalations in urban areas. risk of the extremely low fr...

The class of frequent hypergraph mining problems is introduced which includes the frequent graph mining problem class and contains also the frequent itemset mining problem. We study the computational properties of different problems belonging to this class. In particular, besides negative results, we present practically relevant problems that can be solved in incremental-polynomial time. Some o...

Frequent item set mining is one of the best known and most popular data mining methods. Originally developed for market basket analysis, it is used nowadays for almost any task that requires discovering regularities between (nominal) variables. This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task. It co...

Over the years, frequent itemset discovery algorithms have been used to solve various interesting problems. As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to mo...

Mining association rules is very popular in the data mining community. Most algorithms designed for finding association rules start with searching for frequent itemsets. Typically, in these algorithms, counting phases and pruning phases are interleaved. In the counting phase, partial information about the frequencies of selected itemsets is gathered. In the pruning phase as much as possible of ...

As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discov...

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 ...

In recent years, graph mining has attracted much attention in the data mining community. Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summar...

Premature ventricular contractions (PVCs) are considered normal in many circumstances but can act as markers to other cardiac disease such as cardiomyopathy and ischemic heart disease. This case is presented to illustrate frequent PVCs as a marker for serious heart disease.