By Halimah Badioze Zaman, Peter Robinson, Alan Smeaton, Timothy K. Shih, Sergio Velastin, Azizah Jaafar, Mohamad Ali Nazlena
This publication constitutes the refereed complaints of the Fourth overseas convention on Advances in visible Informatics, IVIC 2015, held in Bangi, Malaysia, in November 2015.
The 5 keynotes and forty five papers provided have been conscientiously reviewed and chosen from eighty two preliminary submissions. The papers are equipped in 4 tracks on visualization and massive info; computer studying and laptop imaginative and prescient; special effects; in addition to digital reality.
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Additional info for Advances in Visual Informatics: 4th International Visual Informatics Conference, IVIC 2015, Bangi, Malaysia, November 17-19, 2015, Proceedings
All experiments were carried out in Matlab on windows 8 with a 2000 MHz processor and 4 GB memory. The execution of aFAmerge, Bisect K-means [4, 9] and pGSCM  is of ten (10) times and the average value of the metrics were recorded. 1 Results and Discussion Table 2 tabularizes the experimental results of Purity, F-measure, and Entropy for the three clustering; aFAmerge, Bisect K-means [4, 9] and pGSCM . As can be seen from data in Table 2, the purity value for aFAmerge is higher than Bisect K-means and pGSCM in both datasets; 20Nwesgroups and Reuters.
The events of extreme increases or decreases in weather are where the temperature is a measure of the warmth or coldness of an object or substance with reference to a standard value . Discovering time series climate change patterns usually used two types of data: data collected from climate stations or satellite operations. Various methods have been used to discover climate change patterns, mainly using statistic and data mining techniques. The statistic method is able to analyse the time series climate change data to discover climate changes using the minimum, maximum, standard deviation, mean, regression © Springer International Publishing Switzerland 2015 H.
Beyond guidelines: what can we learn from the visual information seeking mantra?. In: IV 2005 Proceedings of Ninth International Information Visualisation Conference, pp. 110–118 (2005) 29. : Using qualitative & quantitative research methods to answer your research questions. my Abstract. It is diﬃcult to identify visualized multi-climate change patterns from time series data due to the fact that the data begin to look similar over time. Traditionally, time series weather patterns are presented in the form of a linear graph, which is limited to discovering understandable climate change patterns.