Analysis of Temparament (Arab = Mizaj) by using different Data Mining Techniques (eBook)
60 Seiten
GRIN Verlag
978-3-346-33819-8 (ISBN)
It was tried to classify the data using the various models of classification and found the Naive Bayse (NB) with train set data model showed good classification of the data into four classes with less relative absolute error and compare to J48 model and other models. These are as Bilious, Phlegmatic, Sanguine and Melancholic type. From the confusion matrix it is observed that the data has been correctly classified by Naive Bayse model. As generally the Melancholic type persons are observed very rarely therefore it showed less % of Melancholic type. According to the J48 model, the Mijaz is classified in to three types and these are as bilious, Phlegmatic, sanguine. These are classified depending upon the different attributes such as Sleeping hours, Reaction Moist, Body type, Thorax Shape, occupation and age.
It was also tried to find the relation between the attribute by applying the association rules and using Apriory model with 10 best rules. These shows that there is some strong relation between different attributes such as Reaction strength, Movement, reaction speed and fear etc. Depending upon the level of the anger the person gets troubled. Thus based on the different attributes the temperament is classified into different types.
Further it was tried to cluster the data into different groups, by using the K-Means and EM model. The EM model clustered the data into two types only, which was not correct. There are various classes of temperament of the persons. Mizaj is the same as temperament in Unani Pathy of Arabic. The data is collected on different attributes various backgrounds and field for both male and female persons from Unani Medical College, Pune.
Erscheint lt. Verlag | 1.2.2021 |
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Verlagsort | München |
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Informatik |
Technik | |
Schlagworte | Analysis • Arab • Data • Mining • mizaj • techniques • temparament |
ISBN-10 | 3-346-33819-3 / 3346338193 |
ISBN-13 | 978-3-346-33819-8 / 9783346338198 |
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