Visual data mining modeling techniques for the visualization of mining outcomes

https://doi.org/10.1016/j.jvlc.2003.06.002Get rights and content

Abstract

The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing new visual data mining techniques for the visualization of mining outcomes.

Section snippets

Data mining

The process of searching and analyzing large amounts of data is called “data mining”. The large collections of data are the potential lodes of valuable information but like in real mining, the search and extraction can be a difficult and exhaustive process [1].

Data Mining is a knowledge discovery process of extracting previously unknown, actionable information from very large databases. In details it is the non-trivial extraction of implicit, previously unknown and potentially useful

Research focus

The most common used mean of visualization is the relatively small computer screen, where the total number of data items that can be mapped at one time is limited [7], [12]. The same restriction holds also true even for other representational means such as printed views or virtual worlds where for other additional reasons we have considerable representational limitations. Those restrictions become even more tightening if we consider the pace of growth that characterizes today's datasets. Taking

Visual representation of association rules

In this section, we propose three visual data mining models for the visualization of outcomes produced when mining for association rules. The proposed visualization techniques are based on abstract modelling conceptions applied in the field of data mining. That justifies their reference as visual data mining models. Addressing the problems of this field, our attempt is targeted on facilitating the knowledge extractor to visually analyze and understand a single or a set of rules, along with

Visualizing relevance analysis outcomes

Following the track of our research interest, we proceed in this sub-section on the definition of visual data mining models regarding the representation of outcomes produced by relevance analysis tasks.

Visualizing classification outcomes

On our attempt to graphically reveal the knowledge extracted by a classifier we have mainly based our research effort on the underlying ideas of the geometric projection techniques [19]. From their study we have concluded that those methods seem to be the most promising framework to base our research effort on our attempt to meet the posed requirements in the area of visualizing classification outcomes.

According to the mathematical formalization that we have adopted, a tuple ti, categorized in

Conclusions and future work

With the proposed visual data-mining models our attempt was focused on the invention of visual representations of the outcomes produced by common data mining processes. In order to equip the knowledge engineer with a tool that would be utilized on his/her attempt to gain insight over the mined knowledge, we tried to present as much information extracted in a human perceivable way. Additionally, our basic guideline was that the construction of each visual representation, as long as the

Acknowledgements

Thanks to Dr. Yannis Zorgios for his thoughts and comments on this work and to Dr. Areti Sfrintzeri for her expertise in the medical evaluation scenarios.

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