due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. association rules mining is one of the data mining techniques to extract useful patterns in the form of association rules. one of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. hiding the as...

Many temporal data mining projects use the existing data collected for various purposes, ranging from routinely collected data to process improvement projects and data required for regulatory purposes. In some cases, the set of considered features might be large (a wide data set) and sufficient for extraction of knowledge. In other cases the data set might be narrow and insufficient to extract ...

Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location– Based Services (LBS). To achieve high quality for such services, spatio– temporal data mining techniques are needed. In this paper, we describe experiences with spatio–temporal rule mining in a Danish...

In this paper, we propose a new data mining technique that can address the temporal relation rules of temporal interval data by using Allen’s theory. We present two new algorithms for discovering temporal relationships: one is to preprocess an algorithm for the generalization of temporal interval data and to transform timestamp data into temporal interval data; and the other is to use a tempora...

Data mining is the process of exploring and analyzing data from different perspective, using automatic or semiautomatic techniques to extract knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspe...

Depiction of temporal information and examination of temporal knowledge are essential for effective temporal reasoning in medical analysis systems which are attaining significance recently. These temporal representation models afford attributes for temporal rules mining through association rule extraction which are necessary to perform detective inference in medical expert systems. Existing tem...

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clusteri...

Discovering patterns with great significance is an important problem in data mining discipline. A serial episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Previous studies on serial episodes consider only frequent serial episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each t...

Specification defects are a major source of ambiguities and errors. We propose a new weighted requirement mining framework to address this problem. We define weighted and parametric weighted signal temporal logic (STL) as the underlying formalism to improve the expressiveness. This also improves convergence of the mining process. We provide some primitive results to show the benefit of the new ...