Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally...

Data Mining is a research line that began in 1980 in order to find the knowledge that is hidden in the data that organizations are storing in a daily basis. This knowledge supports the decision-making processes in organizations. As a consequence companies of every kind have been developing data mining projects since the term appeared. However, there is no way to estimate this kind of projects. ...

At Silicon Graphics, Inc., we have developed a data mining and visualization product called MineSet(Silicon Graphics 1998, Brunk, Kelly & Kohavi 1997). MineSet first released in early 1996 mostly as a visualization product and then became a full data mining and visualization product late that year with several data mining algorithms based onMLC++ (Kohavi, Sommerfield & Dougherty 1997). The engi...

Many data mining tools cannot be used directly to analyze the complex sets of relations which are found in large database systems. In our experience, data miners rely on a well-defined data model, or the knowledge of a data expert, to isolate and extract candidate data sets prior to mining the data. For many databases, typically large legacy systems, a reliable data model is often unavailable a...

Although the use of statistically probable properties is very common in the area of medicine, it is not so in software engineering. The use of such properties may open a new avenue for the automated recovery of designs from source codes. In fact, the recovery of designs can also be called program mining, which in turn can be viewed as an extension of data mining to the mining in program source ...

In recent years data mining has become a popular technique for extracting information from the databases of many types in different areas of commerce and industry. The strength of data mining comes from its flexibility of working on any kind of database and its ability to discover previously unknown and sometimes surprising results. Competitiveness increasingly depends on improving the quality ...

A data mining based procedure for automated reverse engineering has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sens...

Process model guidance is an important feature by which the software process is orchestrated. Without complying with this guidance, the production lifecycle deviates from producing a reliable software with high-quality standards. Usually, teams break the process deliberately or impulsively. Application Lifecycle Management (ALM) tools log what teams do even if they break the process. The log fi...