In this paper, an understanding and a review of data mining (DM) development and its applications in logistics and specifically transportation are highlighted. Even though data mining has been successful in becoming a major component of various business processes and applications, the benefits and real-world expectations are very important to consider. It is also surprising to note that very li...

Providing the appropriate access means for data mining services in Grid Environment is principal for combination of Grid and data mining. The transition from centralized data mining process as they are in traditional tools to Grid-compliant and Grid-based data mining services that can coordinate with each other is important to extract useful and potential knowledge/patterns from distributed dat...

Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropri...

This paper presents the Data Mining (DM) toolbox architecture based on cutting edge World Wide Web (WWW) technologies. The DM toolbox is used to discover new and useful knowledge by integrating results generated by multiple DM tools. The proposed architecture allows submission of data to the DM toolbox and generation of results that combine knowledge generated by several different DM tools. The...

CRISP-DM is the standard to develop Data Mining projects. CRISP-DM proposes processes and tasks that you have to carry out to develop a Data Mining project. A task proposed by CRISP-DM is the cost estimation of the Data

Recently, a new data mining methodology, Domain Driven Data Mining (DM), has been developed. On top of data-centered pattern mining, DM generally targets the actionable knowledge discovery under domainspecific circumstances. It strongly appreciates the involvement of domain intelligence in the whole process of data mining, and consequently leads to the deliverables that can satisfy business use...

As an interdisciplinary discipline, data mining (DM) is popular in education area especially when examining students’ learning performances. It focuses on analyzing educational related data to develop models for improving learners’ learning experiences and enhancing institutional effectiveness. Therefore, DM does help education institutions provide highquality education for its learners. Applyi...

The use of all forms of computer and communication devices is changing human interaction and thinking. Electronic traces of actions and activities are continually being left behind most often unknowingly so. This situation creates opportunities for criminal investigators to make use of these traces and marks to uncover evidence. In this evidentiary discovery process several problems are experie...

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs.

Ahsiract-The aim of this paper is to introduce a novel reader to the topic of predictive data mining (DM) by discussing technical aspects and requirements of common mining tools. . A description of DM scope is followed by comparing DM to related data management and analysis techniques. This is followed by a discussion of a typical predictive DM process, and some of the more successful algorithm...