I will describe a novel meta-learning approach to optimizingthe knowledge discovery or data mining (DM) process. This approach hasthree features that distinguish it from its predecessors. First, previousmeta-learning research has focused exclusively on improving the learningphase of the DM process. More specifically, the goal of meta-learning hastypically been to select the ...

An overview of Data Mining (DM) and its application to the analysis of EEG is given by (i) presenting a working deenition of DM, (ii) motivating why EEG analysis is a challenging eld of application for DM technology and (iii) by reviewing exemplary work on DM applied to EEG analysis. The current status of work on DM and EEG is discussed and some general conclusions are drawn.

Data mining (DM) processes require data to be supplied in only one table or data file. Therefore, data stored in multiple relations of relational databases must be joined before submission to DM analysis. A problem faced during this preparation step is that, most of the times, the analyst does not have a clear idea of what portions of data should be mined. This paper reckons the strong human ab...

The number, variety and complexity of projects involving data mining or knowledge discovery in databases activities have increased just lately at such a pace that aspects related to their development process need to be standardized for results to be integrated, reused and interchanged in the future. Data mining projects are quickly becoming engineering projects, and current standard processes, ...

The joint workshop in Data Mining for Environmental Scientists and Intelligent Environmental Decision Support Systems tries to provide a common discussion forum to communicate environmentalists with data miners and intelligent decision support systems developers. As environmentalists are the consumers of both products, Data Mining (DM) and Intelligent Decision Support Systems (IDSS) are complem...

One of the key limitations about research involving big data is the lack of a sound methodological process that drives the conceptual and analytical questions posed to the data. In this study, we adapt the popular CRISP-DM process to analyze large volumes of unstructured data to generate analytical insights. We add specificity to the CRISP-DM methodology. Specifically, we propose “Cross Industr...

An extendable and generic Agent Enriched Data Mining (AEDM) framework, EMADS (the Extendable Multi-Agent Data mining System) is described. The central feature of the framework is that it avoids the use of ontologies or agreed meta-language formats by supporting a system of wrappers. The advantage offered is that the system is easily extendable, further data agents and mining agents can simply b...

In this paper, the knowledge discovery in databases and data mining (KDD/DM), one of the data-based decision support technologies, is applied to help in targeting customers for the insurance industry. In most KDD/DM application cases, major tasks are required, including data preparation, data preprocessing, data mining, interpretation, application and evaluation. A case study is presented that ...

Data Mining is taking out of hidden pattern from a huge database. In data mining, machine learning is mainly focused as research which is mechanically learnt to identify complex patterns and make intelligent decisions based on data. These days Lung Tumor is one of the major causes of death in the developing countries. Today, lung tumor is the most frequent indication for thoracic surgery. By cl...

Basically, Data Mining (DM) and Operations Research (OR) are two paradigms independent of each other. OR aims at optimal solutions of decision problems with respect to a given goal. DM is concerned with secondary analysis of large amounts of data (Hand et al., 2001). However, there are some commonalities. Both paradigms are application focused (Wu et al., 2003; White, 1991). Many Data Mining ap...