Read e-book online Application of Data Mining Techniques in the Analysis of PDF

By Nuno M.M. Ramos, João M.P.Q. Delgado, Ricardo M.S.F. Almeida, Maria L. Simões, Sofia Manuel

ISBN-10: 3319222937

ISBN-13: 9783319222936

ISBN-10: 3319222945

ISBN-13: 9783319222943

The major advantage of the e-book is that it explores to be had methodologies for either undertaking in-situ measurements and correctly exploring the consequences, in keeping with a case research that illustrates the advantages and problems of concurrent methodologies.

The case learn corresponds to a collection of 25 social housing dwellings the place an intensive in situ size crusade was once performed. The dwellings can be found within the comparable sector of a urban. Measurements integrated indoor temperature and relative humidity, with non-stop log in numerous rooms of every residing, blower-door assessments and entire outdoors stipulations supplied by means of a close-by climate station.

The ebook features a number of medical and engineering disciplines, similar to construction physics, likelihood and records and civil engineering. It offers a synthesis of the present nation of data for advantage of specialist engineers and scientists.

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Extra resources for Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions

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3 Tests of normality Normal parameters Most extreme differences Mean Std. 960 Multivariate Data Analysis For the multivariate analysis of the data 11 variables were used: 4 related to indoor temperature, 4 related to the indoor relative humidity, the area (Area), the number of people (N_people) and the airtightness (Rph50). To characterize the internal temperature were used: the daily average (Ti), the 10 % percentile (Ti_10), the 90 % percentile (Ti_90) and the period in which the temperature was below 18 °C (T < 18).

This means that the houses would first be distinguished due to their size even after the parameter normalization. If another option is taken, considering the vertical cut sooner, different clusters can be identified would arise from the two methods. 2 Multivariate Data Analysis 45 Fig. 6 Dendograms of clustering houses by using Ward’s method (left) and average linkage (right) differences in the cluster composition. The sensitivity of Ward’s method to outliers’ identification is clear as in this method dwellings 15 and 17 would be separated from the rest of the houses.

Non-hierarchical methods (often known as k-means clustering methods) • Types of data and measures of distance The data used in cluster analysis can be interval, ordinal or categorical. However, having a mixture of different types of variable will make the analysis more complicated. This is because in cluster analysis you need to have some way of measuring the distance between observations and the type of measure used will depend on what type of data you have. A number of different measures have been proposed to measure ‘distance’ for binary and categorical data.

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Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions by Nuno M.M. Ramos, João M.P.Q. Delgado, Ricardo M.S.F. Almeida, Maria L. Simões, Sofia Manuel

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