This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments ...
This paper develops the concept of usefulness in the context of supervised learning. We argue that usefulness can be used to improve the performance of classification rules (as me...
Gholamreza Nakhaeizadeh, Charles Taylor, Carsten L...
Like model selectionin statistics,the choiceof appropriate Data Mining Algorithms (DM-Algorithms) is a very importanttask in the processof KnowledgeDiscovery.Due to this fact it i...
Discovery of association rules from large databases of item sets is an important data mining problem. Association rules are usually stored in relational databases for future use i...
We consider the problem of aggregation for uncertain and imprecise data. For such data, we define aggregation operators and use them to provide information on properties and patte...
With the increase in information on the World Wide Web it has become difficult to quickly find desired information without using multiple queries or using a topic-specific search ...
Sofus A. Macskassy, Arunava Banerjee, Brian D. Dav...
Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the dat...
Direct marketing is a process of identifying likely buyers of certain products and promoting the products accordingly. It is increasingly used by banks, insurance companies, and t...