Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mini...

A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. ...

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...

Utility mining emerged to overcome the limitations of frequent itemset mining by considering the utility of an item. Utility of an item is based on user’s interest or preference. Recently, temporal data mining has become a core technical data processing technique to deal with changing data. On-shelf utility mining considers on-shelf time period of item and gets the accurate utility values of it...

The essence of data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Existing data mining tools consider snapshots of data and therefore unable to handle the complexity of a dynamic environment, such as financial applications which contain a huge amount of data that changes over time. The knowledge discovered has limited value si...

Utility mining emerged to overcome the limitations of frequent itemset mining by considering the utility of an item. Utility of an item is based on user’s interest or preference. Recently, temporal data mining has become a core technical data processing technique to deal with changing data. On-shelf utility mining considers on-shelf time period of item and gets the accurate utility values of it...

This paper describes an approach to temporal pattern mining using the concept of user defined temporal prototypes to define the nature of the trends of interests. The temporal patterns are defined in terms of sequences of support values associated with identified frequent patterns. The prototypes are defined mathematically so that they can be mapped onto the temporal patterns. The focus for the...

Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing information over temporal data. In this paper, we present a temporal data clustering framework via a weighted clustering produced by initial clustering analysis on different temporal data representations. In the existing system a novel weighted function guided by clustering validatio...

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In a data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered as fast as possible, no blocking operator can be performed and the data can be examined only once. At this time ...