Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like ass...

Web Usage Mining is the process of applying data mining techniques to the discovery of usage patterns from data extracted from Web Log files. In this paper, we define the notion of a “user session” as being a temporally compact sequence of web accesses by a user. We also define a new distance measure between two web sessions that captures the organization of a web site. Web usage mining consist...

association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. association rules help individuals and organizations take strategic decisions and improve their business processes. extracted association rules from a database contain important and confidential information that if published, the privacy of indivi...

Large-scale spatio-temporal data is crucial for testing the spatio-temporal pattern mining algorithms. In this paper we present a data set generator that can generate moving spatio-temporal instances, with evolving regions. The characteristics of data generated can be configured with parameters given to program and those parameters allow users to test the performance of spatio-temporal mining a...

This research demonstrates the application of association rule mining to spatio-temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A → B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio-temporal association rule occurs when there is a spatio-temporal relationship in th...

The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of temporal data. One important example of such processing is detecting anomalies in time-oriented data. The Knowledge-Based Temporal Abstraction method was previously proposed for intelligent interpretation of temporal data based on predefined domai...

For the sake of environmental change monitoring, a huge amount of geospatial and temporal data have been acquired through various networks of monitoring stations. For instance, daily precipitation and air temperature are observed at meteorological stations, and MODIS images are regularly received at satellite ground stations. However, so far these massive raw data from the stations are not full...

The incorporation of temporal semantics into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. Related to this topic, we proposed in [11] an algorithm named TSET for mining temporal patterns (sequences) ...

Functional brain imaging is a source of spatio-temporal data mining problems. A new framework hybridizing multi-objective and multimodal optimization is proposed to formalize these data mining problems, and addressed through Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining are demonstrated as the approach facilitates the modelling of the experts’ requirements, and...