Anomaly detection is the process of finding outlying record from a given data set. This problem has been of increasing importance due to the increase in the size of the data and the need to efficiently extract those outlying records that can have important indications in real-life problems. Anomaly detection is applied in many different sectors. There are many approaches to solve the anomaly detection problem. However, those that are more widely applicable are unsupervised approaches as they do not require labeled data. The aim of this thesis is to study a well-known anomaly detection technique ...

Mining association rules has been an important topic in data mining research in recent years from the standpoint of supporting human-centered discovery of knowledge. The present day model of mining association rules suffers from the following shortcomings: (i) lack of user exploration and control, (ii) lack of focus, (iii) huge number of rules technically unreadable. All data mining researchers, have given a high importance on developing fast algorithms for rules discovery, and have applied different types of constraints in different algorithms to prune item sets, that do not occur frequently ...