Association rule is a data mining technique that has a huge number of applications. One of the crucial steps in association rule is the extraction of frequent itemsets. This research is inspired by simple appealing visualization of itemsets frequencies in the simple well known two dimension matrix representations. This paper proposes a new procedure to extract maximal frequent itemsets called M...

Co-location rule mining is one of the tasks of spatial data mining, which focuses on the detection of sets of spatial features that show spatial associations. Most previous methods are generally based on transaction-free apriori-like algorithms which are dependent on userdefined thresholds and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nont...

Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate for using with educational datasets since they are usually imbalanced. In this paper, we explore the extraction of rare association rules when gathering student usage data from a Moodle system. This type of rule is mor...

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, r...

We discuss the use of database met hods for data mining. Recently impressive results have been achieved for some data mining problems using highly specialized and clever data structures. We study how well one can manage by using general purpose database management systems. We illustrate our ideas by investigating the use of a dbms for a well-researched area: the discovery of association rules. ...

The problem of limited sensor resolution, although usually ignored in target tracking, occurs in multi-target scenarios whenever the target distance falls below the size of the sensor resolution cell. Typical examples are the surveillance of aircraft in formation, and convoy tracking for ground surveillance. Ignoring the limited sensor resolution in a tracking system may lead to degraded tracki...

The human tracking problem is a hot issue in human-robot interaction, in which a conventional algorithm sample-based joint probabilistic data association filters (SJPDAF) is widely used. In this paper, the algorithm is first extended to the situation of multi-sensor fusion and then accelerated to promote the real-time performance. The simulation and experiments on robots both show good results,...

In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake, which causes additional detections. When the detections are fed to a tracking system like the probabilistic data association filter (PDAF), the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even ...

This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association can provide guidance assessing significant relationships that be determined while analyzing The approach uses cardinality sets a minimum and maximum range support confidence metrics. A new notation is used enable representation running example queries about desirable features vacat...