Knowledge discovery in databases (KDD) is an important task in spatial databases since both, the number and the size of such databases are rapidly growing. This paper introduces a set of basic operations which should be supported by a spatial database system (SDBS) to express algorithms for KDD in SDBS. For this purpose, we introduce the concepts of neighborhood graphs and paths and a small set...

Data mining is involved in procedures by which patterns are extracted from data. This process has become increasingly important to map data patterns to useful information that can be used to predict future traffic analyses. Other areas were data mining can be used include: fraud detection, marketing, congestion control, and network expansion consideration. Data mining involves capturing and gat...

In this paper, we report on the use of ant systems in the data mining field capable of extracting comprehensible classifiers from data. The ant system used is a MAX -MIN ant system which differs from the originally proposed ant systems in its ability to explore bigger parts of the solution space, yielding better performing rules. Furthermore, we are able to include intervals in the rules result...

This study presents a data mining analysis of customer forecasting patterns of multiple customers (auto manufacturers) from a large auto parts supplier. We consider a manufacturing environment in which forecasts of future orders are used as inputs for a series of decisions. We define the complexities that are captured from our data set, developing the daily flow analysis to obtain accuracy rati...

Drug–drug interaction is one of the important problems of Adverse Drug Reaction (ADR). This presentation describes a data mining approach to this problem developed at the University of Ballarat. This approach is based on drug–reaction relationships represented in the form of a vector of weights; each vector related to a particular drug can be considered as a pattern in causing adverse drug reac...

Mounting amounts of data made traditional data analysis methods impractical. Data mining (DM) tools provide a useful for alternative framework that addresses this problem. This study follows a DM technique to identify diabetic patients. We develop a model that clusters diabetes patients of a large healthcare company into different subpopulation. Consequently, we show the value of applying a DM ...

Unlike the data approached in traditional data mining activities, software data are featured with partial-repeatability or parepeatics, which is an invariant property that can neither be proved in mathematics nor validated to a high accuracy in physics, but still (partially) governs the behavior of the data. Parepeatics emerges as a result of the inaccurate universe. The universe comprises all ...

Abstract— In this paper, we included the ambitious task of formulating a general framework of data mining. We explained that the framework should fulfil. It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mi...