Elicitation of hidden knowledgeable information from voluminous data is the most prompting technique in data mining (DM). This is proved by DM algorithms for knowledge discovery. Data mining incorporated with Cloud computing technology helps to achieve maximize profit and minimum cost with different possible ways through shared Cloud resource. This paper examines the development framework for i...

This paper introduces the first ontological modeling environment for planning Knowledge Discovery (KDD) workflows. We use ontological reasoning combined with AI planning techniques to automatically generate workflows for solving Data Mining (DM) problems. The KDD researchers can easily model not only their DM and preprocessing operators but also their DM tasks, that are used to guide the workfl...

Most existing data mining (DM) approaches look for patterns in a single table. Multi-relational DM approaches, on the other hand, look for patterns that involve multiple tables. In recent years, the most common DM techniques have been extended to the multi-relational case, but there are few dedicated to star schemas. These schemas are composed of a central fact table, linking a set of dimension...

Current electronic data repositories are growing quickly and contain big amount of data from commercial, scientific, and other domain areas. The capabilities for collecting and storing all kinds of data exceed the abilities to analyze, summarize, and extract knowledge from this data. Knowledge discovery systems (KDSs) use achievements from many technical areas, including databases, Data Mining ...

The effective application of a data mining process is littered with many difficult and technical decisions (i.e. data cleansing, feature transformations, algorithms, parameters, evaluation). Subsequently, most data mining products provide a large number of models and tools, but few provide intelligent assistance for addressing the above-mentioned challenges that face the non-specialist data min...

The application of data mining (DM) in healthcare is increasing. Healthcare organizations generate and collect large voluminous and heterogeneous information daily and DM helps to uncover some interesting patterns, which leads to the manual tasks elimination, easy data extraction directly from records, to save lives, to reduce the cost of medical services and to enable early detection of diseas...

We show how to apply a structured parallel programming (SPP) methodology based on skeletons to data mining (DM) problems, reporting several results about three commonly used mining techniques, namely association rules, decision tree induction and spatial clustering. We analyze the structural patterns common to these applications, looking at application performance and software engineering effic...

Previous work on the solution to analytical electronic customer relationship management (eCRM) problems has used either data-mining (DM) or optimization methods, but has not combined the two approaches. By leveraging the strengths of both approaches, the eCRM problems of customer analysis, customer interactions, and the optimization of performance metrics (such as the lifetime value of a custom...

Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) and Workflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus...