These explanations can be opaque—providing a means of classifying variation without any explanation of why it works— or transparent—describing what creates the observed variation. For two reasons, data mining could be the killer application that parallel computing has been seeking. First, analyzing variation appears to be algorithmically complex and hence might require levels of computing power...

There has been a large amount of research work done on mining on relational databases that store data in exact values. However, in many real-life applications such as those commonly used in service industry, the raw data are usually uncertain when they are collected or produced. Sources of uncertain data include readings from sensors (such as RFID tagged in products in retail stores), classific...

Temporal Mining Algorithms: Generalization and Performance Improvements Data mining consists of finding interesting trends or patterns in large datasets, in order to guide decisions about future activities. There is a general expectation that data mining tools should be able to identify these patterns in the data with minimal user input. The patterns identified by such tools can give a data ana...

Advanced personalized e-applications require comprehensive knowledge about their user’s likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. ...

In recent years, due to the expansion of financial institutions,as well as the popularity of the World Wide Weband e-commerce, a significant increase in the volume offinancial transactions observed. In addition to the increasein turnover, a huge increase in the number of fraud by user’sabnormality is resulting in billions of dollars in lossesover the world. T...

clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. k-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. in recent years, several algorithms are provided based on evolutionary algorithms for cluster...

Mining large data set is an important issue to deal with as data is growing as the field grows. Today, crime rate is a menace that each country faces. With the increase in crime rate the data is increasing and it is such a critical field that accuracy is important at the same time. This paper shows the comparison in the results between clustering and the classification. K means is used in clust...