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SIPINA is known for its decision tree induction algorithms. In fact, the distribution includes two other tools that are little known to the public: REGRESS, which is specialized in multiple linear regression, we described it in one of our tutorials ; and an association rules extraction tool, called simply Association Rule Software (ARS). In this tutorial, I describe the use of the ARS tool. Its interactivity with Excel spreadsheet is its main advantage. We launch the software from Excel using the “sipina.xla” add-in. We can easily retrieve the rules in the spreadsheet. Then, we can explore them (the mined rules) using the Excel data handling capabilities. The ability to filter and sort rules according to different criteria is a great help in detecting interesting rules. This is a very important aspect because the profusion of rules can quickly confuse the data miner. Keywords : ARS, association rule software, excel spreadsheet, filtering and sorting rules, interestingness measures Comp...

Association rule learning is a popular approach to extract rules from large databases. Initially intended to transactional data, especially for the market basket analysis, the method can be applied to any binary or binarized data. In these slides, we show the outline of the approach. We present a basic algorithm to generate association rules from data. We highlight the influence of the settings (minimum support and minimum confidence) for the reduction of the search space, and thus for the reduction  of the amount of calculations. Keywords : association rule, association rules, itemset, frequent itemset, eclat algorithm, support, confidence, lift Components (Tanagra) : A PRIORI, A PRIORI MR, A PRIORI PT, FREQUENT ITEMSETS, SPV ASSOC RULE, SPV ASSOC TREE Slides : Association rule learning References : Wikipedia, " Association Rule Learning ". M. Zaki, S. Parthasaraty, M. Ogihara, W. Li, “ New Algorithms for Fast Discovery of Association Rules ”, in Proc. of KDD’97, p. 283-296,...

Searching regularities from dataset is the main goal of the data mining. They may have various representations. In the market basket analysis, we search the co occurrences of goods (items) i.e. the goods which are often purchased simultaneously. They are called “frequent itemset”. For instance, one result may be "milk and bread are purchased simultaneously in 10% of caddies". Frequent itemset mining is often presented as the preceding step of the association rule learning algorithm. At the end of the process, we highlight the direction of the relation. We obtain rules. For instance, a rule may be "90% of the customers which buy milk and bread will purchase butter also". This kind of rule can be used in various manners. For instance, we can promote the sales of milk and bread in order to increase the sales of butter. In fact, frequent itemsets provide also valuable information. Detecting the goods which are purchased simultaneously enables to understand the relation ...

Association Rule Software (ARS) is a basic tool which extracts association rules from attribute-value datasets (categorical or binary attributes). It is distributed with the SIPINA package which includes: a tool for the supervised learning framework, especially the decision tree induction (SIPINA RESEARCH); a tool for the linear regression (REGRESS); and thus, ARS for the association rule mining. ARS encodes automatically the categorical attributes in dummy variables. If you want use a continuous attributes, you must discretize them before. This tutorial describes shortly the use of the Association Rule Software (ARS). Compared with the previous version, the GUI of the one incorporated into the SIPINA 3.8 package is simplified. Keywords : association rule mining, support, confidence, lift, conviction Download : Sipina setup file Tutorial : How to use ARS References : Wikipedia - Association rule learning

The tools (SIPINA RESEARCH, REGRESS and ASSOCIATION RULE SOFTWARE) included in the SIPINA distribution have been updated with some improvements. SIPINA.XLA . The add-in for Excel can work now with either for the 32 or 64-bit versions of EXCEL. Importation of text data files . Processing time has been improved. This improvement reduces also the transferring time when we use the SIPINA.XLA add-in for Excel (which uses a temporary file in the text file format). Association rule software . The GUI has been simplified; the display of the rules is made more readable. Because they are internally based on the FastMM memory management, these tools can address up to 3 GB under 32-bit Windows and 4 GB under 64-bit Windows . The processing capabilities are improved. Keywords : sipina, decision tree induction, association rule, multiple linear regression Sipina website : Sipina Download : Setup file References : Tanagra - SIPINA add-in for Excel Tanagra - Tanagra add-in for Excel 2007 and 2010 Delp...

A PRIORI PT is a tool dedicated for the extraction of association rules. This is one of the few components of Tanagra based on external library. We use the Borgelt's "apriori.exe" program. Until the version 1.4.40 of Tanagra, we used the 4.31 version of "apriori.exe". From the Tanagra 1.4.41 , we introduce the latest update 5.57 (2011/09/02). Even if the settings of the tool are slightly modified, we observe that the extracted rules and the readings of the results are identical. We take again a former tutorial to describe the behavior of this component ( Association Rule Learning using A PRIORI PT ). Thus, we do not detail the construction of the diagram here. We try above all to highlight the improvement of the library, especially about the computation time. We observe that this improvement is really impressive. Keywords : association rule, large dataset Components : A priori PT Tutorial : en_Tanagra_AprioriPT_Updated.pdf Dataset : assoc_census.zip Reference : ...

A PRIORI PT . This component generates association rules. It is based on the Borgelt's apriori.exe program which has been recently updated (2011/09/02 - 5.57 version). The improvement of this new version, in terms of calculation time, is impressive. FREQUENT ITEMSETS . Also based on the Borgelt's apriori.exe program (version 5.57), this component generates frequent (or closed, maximum, generators) itemsets. Some tutorials are coming soon to describe the use of these new tools. Donwload page : setup

Association rule learning is a popular method for discovering interesting relations between variables in large databases. It was often used in market basket analysis domain. But in fact, it can be implemented in various areas where we want to discover the associations between variables. The association is described by a "IF THEN" rule. The IF part is called "antecedent" of the rule; the THEN part correspond to the "consequent" e.g. IF onions AND potatoes THAN burger ( http://en.wikipedia.org/wiki/Association_rule_learning ) i.e. if a customer buys onions and potatoes then he buys also burger. It is possible to find co-occurrences in the standard attribute - value tables that are handled with the most of the data mining tools. In this context, the rows correspond to the baskets (transactions); the columns correspond to the list of all possible products (items); at the intersection of the row and the column, we have an indicator (true/false or 1/0) which ind...