The Coron System

Coron is a domain and platform independent, multi-purposed data mining toolkit, which incorporates not only a rich collection of data mining algorithms, but also allows a number of auxiliary operations. To the best of our knowledge, a data mining toolkit designed specifically for itemset extraction and association rule generation like Coron does not exist elsewhere. Coron also provides support for preparing and filtering data, and for interpreting the extracted units of knowledge

A Novel Algorithm for Discovering Frequent Closures and Generators

The Important construction of many association rules needs the calculation of Frequent Closed Item Sets and Frequent Generator Item Sets (FCIS/FGIS). However, these two odd jobs are joined very rarely. Most of the existing methods apply level wise Breadth-First search. Though the Depth-First search depends on different characteristics of data, it is often better than others. Hence, in this paper it is named as FCFG algorithm that combines the Frequent closed item sets and frequent generators. This proposed algorithm (FCFG) extracts frequent itemsets (FIs) in a Depth-First search method. Then this algorithm extracts FCIS and FGIS from FIs by a level wise approach. Then it associates the generators to their closures. In FCFG algorithm, a generic technique is extended from an arbitrary FI-miner algorithm in order to support the generation of minimal non-redundant association rules. Experimental results indicate that FCFG algorithm performs better when compared with other level wise methods in most of the cases

An Efficient Hybrid Algorithm for Mining Frequent Closures and Generators

Conference site: http://cla2008.inf.upol.cz/ .International audienceThe effective construction of many association rule bases requires the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal, depending on data characteristics, is often superior. Hence, we address here a hybrid algorithm that combines the two different traversals. The proposed algorithm, Eclat-Z, extracts frequent itemsets (FIs) in a depth-first way. Then, the algorithm filters FCIs and FGs among FIs in a levelwise manner, and associates the generators to their closures. In Eclat-Z we present a generic technique for extending an arbitrary FI-miner algorithm in order to support the generation of minimal non-redundant association rules too. Experimental results indicate that Eclat-Z outperforms pure levelwise methods in most cases

Efficient Mining of Frequent Closures with Precedence Links and Associated Generators

The effective construction of many association rule bases require the computation of frequent closures, generators, and precedence links between closures. However, these tasks are rarely combined, and no scalable algorithm exists at present for their joint computation. We propose here a method that solves this challenging problem in two separated steps. First, we introduce a new algorithm called Touch for finding frequent closed itemsets (FCIs) and their generators (FGs). Touch applies depth-first traversal, and experimental results indicate that this algorithm is highly efficient and outperforms its levelwise competitors. Second, we propose another algorithm called Snow for extracting efficiently the precedence from the output of Touch. To do so, we apply hypergraph theory. Snow is a generic algorithm that can be used with any FCI/FG-miner. The two algorithms, Touch and Snow, provide a complete solution for constructing iceberg lattices. Furthermore, due to their modular design, parts of the algorithms can also be used independently

Current Developments and Problems of Electricity Regulation in the European Union and the United Kingdom

This paper deals current developments and the problems of regulation in European electricity in general and -in somewhat more detail-, England and Wales in particular.electricity

Finding frequent closed itemsets with an extended version of the Eclat algorithm

Apriori is the most well-known algorithm for finding frequent itemsets (FIs) in a dataset. For generating interesting association rules, we also need the so-called frequent closed itemsets (FCIs) that form a subset of FIs. Apriori has a simple extension called Apriori-Close that can filter FCIs among FIs. However, it is known that vertical itemset mining algorithms outperform the Apriori-like levelwise algorithms. Eclat is another well-known vertical miner that can produce the same output as Apriori, i.e. it also finds the FIs in a dataset. Here we propose an extension of Eclat, called Eclat-Close that can filter FCIs among FIs. This way Eclat-Close can be used as an alternative of Apriori-Close. Experimental results show that Eclat-Close performs much better than Apriori-Close, especially on dense, highly-correlated datasets