Not only since their continuance of operation, had the accumulation of historical error data of colliery equipments resulted in a mass of surplus data, but also because their lacks of helpful information, which as a result makes colliery managers as well as equipment operators hard to make decisions.
Seeing that, we introduced ways here that makes use of data mining technology by processing and analyzing historical monitoring data, recognizing and extracting meaningful patterns so as to provide scientific information for decision-making on the safety of colliery operations, which would help for the forecasting of potential threatens of colliery equipments’ operation, thus, make great contributions to prevent disasters from happening.
The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
Data mining tools can answer business questions that traditionally were too time-consuming to resolve.
Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line.
Data mining is the semi-automatic discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data.
E-commerce is kind of new business model,web data mining technology is an important new area of research applications in e-commerce.
This article described the concepts and types of e-commerce and Web data mining technology and analyzed how to proceed Web data mining in e-commerce, elaborated Web data mining technology in e-commerce.
That is, data mining attempts to extract knowledge from data.
Data mining differs from traditional statistics in several ways: formal statistical inference is assumption driven in the sense that a hypothesis is formed and validated against the data.