The Web has become a fertile ground for numerous research activities in mining. In this chapter we discuss research on finding targeted information on the Web. First, we briefly survey the research area. We focus in particular on two key issues: (a) mining to impose structures over Web data, for example by building taxonomies and portals, to aid in Web navigation, and (b) mining to build inform...

While “big data” has become a highlighted buzzword since last year, “big data mining”, i.e., mining from big data, has almost immediately followed up as an emerging, interrelated research area. This paper provides an overview of big data mining and discusses the related challenges and the new opportunities. The discussion includes a review of state-of-the-art frameworks and platforms for proces...

The article describes the basic principles and methods of Data mining and Process mining, their similarities and differences. The authors examine the research in Educational Data Mining field, associated with the use of Data mining techniques in education, give examples of problems to be solved with the use of Data mining and Process mining techniques in the area of traditional and e-learning, ...

The hardness of finite domain Constraint Satisfaction Problems (CSPs) is a very important research area in Constraint Programming (CP) community. However, this problem has not yet attracted much attention from the researchers in the association rule mining community. As a popular data mining technique, association rule mining has an extremely wide application area and it has already been succes...

Frequent pattern discovery is a heavily focused area in data mining. Discovering concealed information from Web log data is called Web usage mining. Web usage mining discovers interesting and frequent user access patterns from web logs. This paper contains a novel approach, based on k-mean and frequent pattern tree (FP-tree), for frequent pattern mining from Weblog data.

The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians. In this paper, we focus on the implementation of Multiple-Instance Learning (MIL) in the area of medical image mining, particularly to hard exudates detection in retinal images from diabetic patien...