In this paper, we have focused to compare a variety of techniques, approaches and different tools and its impact on the healthcare sector. The goal of data mining application is to turn that data are facts, numbers, or text which can be processed by a computer into knowledge or information. The main purpose of data mining application in healthcare systems is to develop an automated tool for ide...

Sequential pattern mining is the process of applying data mining techniques to a sequential database, to extract frequent subsequences to discover correlation that exists among the ordered list of events. Web Usage mining (WUM) discovers and extracts interesting knowledge/patterns from Web logs is one of the applications of Sequential Pattern Mining. In this paper, we present a survey of the se...

Web data mining techniques are becoming popular and valuable components of web data analysis systems. It assists website’s owners to estimate their website’s performance and make explicit and precise business strategies. The main features of Decisional DNA are related to knowledge representation structures. They are dealing with noisy and incomplete data, learning from experience, making precis...

Data acquisition and maintenance to any industry is multiplying at an enormous speed. In any industry the produced data is not only massive but also quite complex. As a result, proper handling of data is of prime importance in order to convert the available data into useful information that leads to knowledge and apposite decision making. Use of data mining in the healthcare industry is proving...

Decision support systems (DSSs) are prevalent information systems for decision making in many competitive business environments. In a DSS, decision making process is intimately related to some factors which determine the quality of information systems and their related products. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers ne...

Aranea is a question answering system that extracts answers from the World Wide Web using knowledge annotation and knowledge mining techniques. Knowledge annotation, which utilizes semistructured database techniques, is effective for answering large classes of commonly occurring questions. Knowledge mining, which utilizes statistical techniques, can leverage the massive amounts of data availabl...

Recently, process mining has become a vivid research area [5, 6]. The basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Process mining techniques and tools provide the means for discovering process, control, data, organizational, and social structures from event logs. In this paper we provide an overview of process mining techniques/tools and the...

In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power availa...

Knowledge discovery in databases or data mining is the semiautomated analysis of large volumes of data, looking for the relationships and knowledge that are implicit in large volumes of data and are ’interesting’ in the sense of impacting an organization’s practice. Data mining and knowledge discovery on large amounts of data can benefit of the use of parallel computers both to improve performa...