Graph pattern mining aims at identifying structures that appear frequently in large graphs, under the assumption that frequency signifies importance. Several measures of frequency have been proposed that respect the apriori property, essential for an efficient search of the patterns. This property states that the number of appearances of a pattern in a graph cannot be larger than the frequency ...

In this paper we introduce a sequential block iterative method and its simultaneous version with op-timal combination of weights (instead of convex combination) for solving convex feasibility problems.When the intersection of the given family of convex sets is nonempty, it is shown that any sequencegenerated by the given algorithms converges to a feasible point. Additionally for linear feasibil...

Due to the large number of genes measured in a typical microarray dataset, feature selection plays an essential role in tumor classification. In turn, relevance and redundancy are key components in determining the optimal predictor set. However, a third component – the relative weights given to the first two also assumes an equal, if not greater importance in feature selection. Based on this th...

Background and Objectives. Values in the known weighting methods in Life Cycle Assessment are mostly founded by the societal systems of developed countries. What source of weights and which weighting methods are reliable for a big developing country like China? The purpose of this paper is to find a possible weighting method and available data that will work well for LCA practices conducted in ...

We report body weights (BW) and blood and serum analyses for 6 fully mature and 8 not-yet-mature captive plains viscachas before and 3, 6 and 9 months after switching from a low-fiber, high-energy diet to a high-fiber, low-energy diet. Initially, body weights, serum glucose, fructosamine and cholesterol levels were above the reference range in the fully mature animals. Furthermore, 4 of these a...

Clustering of data streams is an important issue in data mining. A large number of algorithms exist for clustering data streams but most of these algorithms give equal weights to all the dimensions of the data stream. Some of the dimensions of the data stream may play important role in clustering while some may be just useless. In this paper, we introduce a density based algorithm in which the ...

The pairwise comparison method is an interesting technique for building a global ranking from binary comparisons. In fact, some web search engines use this method to quantify the importance of a set of web sites. The purpose of this paper is to search a set of priority weights from the preference information contained in a general pairwise comparison matrix; i.e., a matrix without consistency a...