Geobrowsing: Creative Thinking and Knowledge Discovery Using Geographic Visualization

2002 ◽  
Vol 1 (1) ◽  
pp. 80-91 ◽  
Author(s):  
Donna J. Peuquet ◽  
Menno-Jan Kraak
Keyword(s):  
Computer Graphics ◽  
Knowledge Discovery ◽  
Creative Thinking ◽  
Visual Image ◽  
Visual Display ◽  
Geographic Visualization ◽  
Real Power ◽  
New Information ◽  
Large Databases ◽  
Product Maps

In the modern computing context, the map is no longer just a final product. Maps are now being used in a fundamentally different way – as a self-directed tool for deriving the desired information from geographic data. This, along with developments in GIScience and computer graphics, have led to the new field of geographic visualization. A central issue is how to design visualization capabilities that, as a process, facilitate creative thinking for discovering previously new information from large databases. The authors propose the term ‘geobrowsing’ to designate this process. A number of interrelated ways that visualization can be used to spark the imagination in order to derive new insights are discussed and a brief example provided. Based upon the cognitive literature, specific properties of a visual image that promote discovery and insight are discussed. These are known as preinventive properties, and include; novelty, incongruence, abstraction, and ambiguity. All of these properties, either individually or in combination, tend to produce features that are unanticipated by the viewer, and often not explicitly created or anticipated by the person generating the visual display. While traditional (i.e. non-computer generated) images can also possess these properties, as shown in the historical examples in this discussion, it is the capability of the viewer to directly and quickly manipulate these properties that provides the real power of ‘geobrowsing’ for uncovering new insights.

Discovering Frequent Embedded Subtree Patterns from Large Databases of Unordered Labeled Trees

2008 ◽  
pp. 3235-3251
Author(s):  
Yongqiao Xiao ◽  
Jenq-Foung Yao ◽  
Guizhen Yang
Keyword(s):  
Knowledge Discovery ◽  
Web Sites ◽  
Synthetic Data ◽  
Research Interest ◽  
Data Sets ◽  
Labeled Trees ◽  
Computational Overhead ◽  
Large Databases ◽  
Frequency Counts ◽  
Novel Algorithm

Recent years have witnessed a surge of research interest in knowledge discovery from data domains with complex structures, such as trees and graphs. In this paper, we address the problem of mining maximal frequent embedded subtrees which is motivated by such important applications as mining “hot” spots of Web sites from Web usage logs and discovering significant “deep” structures from tree-like bioinformatic data. One major challenge arises due to the fact that embedded subtrees are no longer ordinary subtrees, but preserve only part of the ancestor-descendant relationships in the original trees. To solve the embedded subtree mining problem, in this article we propose a novel algorithm, called TreeGrow, which is optimized in two important respects. First, it obtains frequency counts of root-to-leaf paths through efficient compression of trees, thereby being able to quickly grow an embedded subtree pattern path by path instead of node by node. Second, candidate subtree generation is highly localized so as to avoid unnecessary computational overhead. Experimental results on benchmark synthetic data sets have shown that our algorithm can outperform unoptimized methods by up to 20 times.

Knowledge Discovery with Artificial Neural Networks

2011 ◽  
pp. 669-673
Author(s):  
Juan R. Rabuñal Dopico ◽  
Daniel Rivero Cebrian ◽  
Julián Dorado de la Calle ◽  
Nieves Pedreira Souto
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Discovery ◽  
Powerful Technique ◽  
Complex Problems ◽  
New Information ◽  
Wide Range ◽  
The World ◽  
Artificial Neural

The world of Data Mining (Cios, Pedrycz & Swiniarrski, 1998) is in constant expansion. New information is obtained from databases thanks to a wide range of techniques, which are all applicable to a determined set of domains and count with a series of advantages and inconveniences. The Artificial Neural Networks (ANNs) technique (Haykin, 1999; McCulloch & Pitts, 1943; Orchad, 1993) allows us to resolve complex problems in many disciplines (classification, clustering, regression, etc.), and presents a series of advantages that convert it into a very powerful technique that is easily adapted to any environment. The main inconvenience of ANNs, however, is that they can not explain what they learn and what reasoning was followed to obtain the outputs. This implies that they can not be used in many environments in which this reasoning is essential.

Presence within Virtual Environments as a Function of Visual Display Parameters

1996 ◽  
Vol 5 (3) ◽  
pp. 274-289 ◽  
Author(s):  
Claudia Hendrix ◽  
Woodrow Barfield
Keyword(s):  
Virtual Environments ◽  
Virtual Environment ◽  
Virtual World ◽  
Visual Image ◽  
Visual Display ◽  
Field Of View ◽  
Head Tracking ◽  
The Third ◽  
Sense Of Presence ◽  
Being There

This paper reports the results of three studies, each of which investigated the sense of presence within virtual environments as a function of visual display parameters. These factors included the presence or absence of head tracking, the presence or absence of stereoscopic cues, and the geometric field of view used to create the visual image projected on the visual display. In each study, subjects navigated a virtual environment and completed a questionnaire designed to ascertain the level of presence experienced by the participant within the virtual world. Specifically, two aspects of presence were evaluated: (1) the sense of “being there” and (2) the fidelity of the interaction between the virtual environment participant and the virtual world. Not surprisingly, the results of the first and second study indicated that the reported level of presence was significantly higher when head tracking and stereoscopic cues were provided. The results from the third study showed that the geometric field of view used to design the visual display highly influenced the reported level of presence, with more presence associated with a 50 and 90° geometric field of view when compared to a narrower 10° geometric field of view. The results also indicated a significant positive correlation between the reported level of presence and the fidelity of the interaction between the virtual environment participant and the virtual world. Finally, it was shown that the survey questions evaluating several aspects of presence produced reliable responses across questions and studies, indicating that the questionnaire is a useful tool when evaluating presence in virtual environments.

scholarly journals Proses Pengambilan Keputusan

2019 ◽  
Author(s):  
yola febriani ◽  
Hade Afriansyah ◽  
Rusdinal
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Literature Review ◽  
Creative Thinking ◽  
Human Life ◽  
Decision Makers ◽  
New Information ◽  
The Right ◽  
Alternative Solutions ◽  
Made In

This article aims to describe how is the process of decision making. Decision making is something that is never separated from human life, both simple decision making and complex problems. Everyone is always faced with the choice to take a decision. To be able to take the right decisions, every person should know the steps. This article presents what the decision-making steps and what is the importance of creative thinking in decision making. Creative thinking will help decision makers to improve the quality and effectiveness of problem solving and decision making results were made. In relation to the process of decision making, creative thinking is needed, especially in identifying problems and develop alternative solutions. The methodology used to arrange this article is Systematic Literature Review (SLR). First, researcher find relevant theories, and then make a conclusion about it, then analyzing, and finally make a new information based researcher analyzing.

scholarly journals Proses Pengambilan Keputusan

2019 ◽  
Author(s):  
yola febriani ◽  
Hade Afriansyah ◽  
Rusdinal
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Literature Review ◽  
Creative Thinking ◽  
Human Life ◽  
Decision Makers ◽  
New Information ◽  
The Right ◽  
Alternative Solutions ◽  
Made In

This article aims to describe how is the process of decision making. Decision making is something that is never separated from human life, both simple decision making and complex problems. Everyone is always faced with the choice to take a decision. To be able to take the right decision, every person should know the steps. This article presents what the decision making steps and what is the importance of creative thinking in decision making. Creative thinking will help decision makers to improve the quality and effectiveness of problem solving and decision making results were made. In relation to the process of decision making, creative thinking is needed, especially in identifying problems and develop alternative solutions. The methodology used to arrange this article is Systematic Literature Review (SLR). First, researcher find relevant theories, and then make a conclusion about it, then analyzing, and finally make a new information based researcher analyzing

scholarly journals An Application of J48 Classification Algorithm in Predicting Students’ Academic Performance

2020 ◽  
Vol 9 (4) ◽  
pp. 1172-1176
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Knowledge Discovery ◽  
Classification Algorithm ◽  
Knowledge Discovery In Databases ◽  
Struggling Students ◽  
Correlational Design ◽  
New Information ◽  
Exploratory Data ◽  
Students Success

This paper sets out to use J48 classification algorithm to predict students’ academic performance towards the end of the semester in the Data Structure course under the Computer Science Program. This algorithm aimed to help faculty in forecasting who among the students would likely to fail and who would make it until the end of the semester. In this way, the faculty could make remedial measures to help those struggling students pass the subject and advance to the next level, thus, increasing students’ success rate and retention in a Higher Education Institutions (HEI). This research employed a descriptive correlational design using Exploratory Data Analysis (EDA) for Data Mining in testing and verifying data to generate new information. Data mining is part of the Knowledge Discovery in Databases (KDD) process where it follows six steps: data selection, data pre-processing, data transformation, data mining, interpretation, and knowledge discovery. Step 1 includes gathering and selecting data for the study and for this purpose, a total of 103 students’ records were collected from the instructors for a period of two semesters, S.Y. 2014 -2015 & 2015 – 2016. Different evaluative criteria contained in the class records were utilized as attributes in predicting students’ academic performance. Steps 2 and 3 is pre-processing and transforming the data where it involves discarding those students who dropped/withdrawn from the semester, and converting the excel file into a comma separated values or .csv file, respectively. After these steps, step 4 or the application of J48 classification algorithm was utilized to discover classification rules. Step 5 refers to the tree visualization results where it identified the strongest predictor that most likely influence the students’ final average grade. Finally, step 7 shows the extracted information from the tree or the extracted rules that can be used by the administration, faculty and other stakeholders to improve the academic performance of the students. In particular, they might consider redesigning and restructuring teaching pedagogies to assist and focus more on struggling students.

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