The incorporation of temporal semantics into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. Related to this topic, we proposed in [11] an algorithm named TSET for mining temporal patterns (sequences) ...

Text Mining is an important step of Knowledge Discovery process. It is used to extract hidden information from notstructured o semi-structured data. This aspect is fundamental because much of the Web information is semi-structured due to the nested structure of HTML code, much of the Web information is linked, much of the Web information is redundant. Web Text Mining helps whole knowledge minin...

Data Mining as a process of finding new, useful knowledge from data using different techniques. Using these techniques we getting faster and better search of large amounts of data that we facing every day. Clustering of data is one of the techniques that are used in data mining. Authors explore clustering algorithms and take suffix tree clustering algorithm for the best of them. Authors create ...

Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs abundantly available in today’s information systems. Although process mining has been applied in hundreds of organizations and process mining techniques have been embedded in a variety of commercial tools, to date these techniques have rarely been used for analyzing web services. One of t...

The problem of enhancing a database management system(DBMS) to support mining applications is twofold. First DBMSs of today have limited functionality for supporting mining applications. Second scaling traditional knowledge discovery techniques for large data sets is not straight forward. Our goal is to propose a system architecture for future DBMSs that incorporate interactive modules for data...

The valuable knowledge can be discovered through data mining process. In data mining, classification is one of the major tasks to impart knowledge from huge amount of data. This technique is widely used in various fields, but it has not attracted much attention in Human Resource Management (HRM). This article presents a study on the implementation of data mining approach for employee developmen...

R.C. Jain Department of Computer Applications, Samrat Ashok Technological Institute, Vidisha, M.P. E-mail: [email protected] -------------------------------------------------------------------ABSTRACT----------------------------------------------------------------Association rule mining and classification are two important techniques of data mining in knowledge discovery process. Integration...

the application of data mining techniques on educational systems has received increased attention on research community. The data from courses management systems are a rich source for knowledge discovery, where data mining techniques can be applied, such as: classification, clustering, association rules and mining of text patterns. The results may be useful for evaluation and development educat...

Data mining is part of the interdisciplinary field of knowledge discovery in databases. Research on data mining began in the 1980s and grew rapidly in the 1990s.Specific techniques that have been developed within disciplines such as artificial intelligence, machine learning and pattern recognition have been successfully employed in data mining. Data mining has been successfully introduced in ma...

Due to recent technological development knowledge has been playing a significant role on human activities. Data can be stored and maintained to generate useful information. Due to the increase in the data, it is important to extract knowledge/information from such large data repositories Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspecti...