There are various classes of temperament of the persons. Mizaj is the same as temperament in Unani Pathy of Arabic. Here we have collected the data on different attributes various backgrounds and field for both male and female persons from Unani Medical College, Pune. We have tried to apply the Data mining rule Classification, Association and Clustering by using the WEKA as Data Mining tool.
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.
Table of Contents
1. Introduction
1.1 Business Application
1.2 Data Structure
1.3 Tasks and methods
1.4 Import and Export of Data and models
1.5 Categorization of Data Mining Software into Different Types
1.6 Mijaz
1.7 Research Methodology
2. An Introduction to the WEKA Data Mining System
2.1 Data Mining
2.2 Data Mining Software
2.3 Weka Data Mining Software
2.4 Data preprocessing and visualization
2.5 Attribute Selection
2.6 Errors Rules Attribute
2.7 Classification – decision tree
2.8 Clustering – k-means
2.9 Association Rules
3. Data Collection
3.1 Brief Description
3.2 Attributes
3.3 Data Processing
4. Result Analysis and Discussions
4.1 Classification
4.2 Association
4.3 Clustering
4.4 Knowledge Flow for NB Tree Model
5. Conclusion
5.1 Future Scope of the Work
6. Reference
Research Objectives and Focus Areas
The primary objective of this study is to analyze and classify human temperaments (Mizaj) based on various personal attributes collected from individuals, utilizing advanced data mining techniques. The research aims to determine the relationships between these attributes and the four distinct classes of temperament, identifying the most accurate classification and association models to map these characteristics effectively.
- Application of WEKA as a primary data mining tool for temperament analysis.
- Comparative evaluation of classification models, including Naive Bayes, J48, and NB Tree.
- Association rule mining to discover correlations between personal and physiological attributes.
- Clustering analysis to identify patterns and groupings within the temperament data.
Excerpt from the Book
1. Introduction
Data mining has a long history, with strong roots in statistics, artificial intelligence, machine learning, and database research (1, 2). Advancements in this field were accompanied by development of related software tools, starting with mainframe programs for statistical analysis in the early 1950s, and leading with to a large variety of stand alone, client/server and web based software as today’s service solution
Today, a large number of standard data mining methods are available (3,4) from historical perspective. These methods have different roots. There are several different and sometime overlapping categorizations for example, fuzzy logic, artificial neural networks, and evolutionary algorithms, which are summarized as computational intelligence (5).
The life cycle of new data mining method begins with theoretical paper based on inhouse software prototypes, followed by public or on demand software distribution of successful algorithms as research prototypes. Then, special commercial or open source packages containing a family of similar algorithms are developed or the algorithms are integrated into exiting open source or commercial packages. Many companies have tried to promote their own alone packages, but only few have reached notable market shares. The life cycle of some data mining tools is remarkable short. This may be due to internal marketing decision and acquisitions of specialized companies by larger ones, leading to a reaming and integration of product lines.
Summary of Chapters
1. Introduction: Outlines the history and lifecycle of data mining methods, discusses the categorization of software tools, and defines the concept of Mijaj (temperament) within the context of the study.
2. An Introduction to the WEKA Data Mining System: Details the functionalities of the WEKA software, including data preprocessing, attribute selection, and the specific classification, clustering, and association algorithms available for analysis.
3. Data Collection: Describes the methodology for gathering observational data from 67 instances and 37 attributes from Unani Medical College, including the conversion of data formats for WEKA compatibility.
4. Result Analysis and Discussions: Presents the statistical findings from the classification, association, and clustering models, highlighting the superiority of the Naive Bayes and NB Tree models for accurate temperament prediction.
5. Conclusion: Summarizes the research outcomes, confirming the classification of temperament into four classes and outlining the potential for future implementation of complex clustering algorithms.
6. Reference: Provides a list of academic sources and literature utilized to support the data mining research and its application.
Keywords
Data Mining, WEKA, Temperament, Mijaj, Classification, Naive Bayes, Clustering, Association Rules, Apriory Algorithm, NB Tree, Unani Pathy, J48, Statistical Analysis, Feature Selection, Knowledge Flow
Frequently Asked Questions
What is the core focus of this research?
The research focuses on utilizing data mining techniques to analyze and classify human temperament (Mizaj) based on collected physiological and behavioral attributes.
Which specific data mining tool is used in the study?
The study exclusively uses WEKA (Waikato Environment for Knowledge Analysis) as the data mining tool for processing, classification, and clustering.
What is the primary objective of this work?
The primary objective is to classify data into temperament types accurately and to discover strong correlations between personal attributes using machine learning models.
What scientific methods were employed?
The authors employed classification (Naive Bayes, J48, NB Tree), association rule mining (Apriory), and clustering (K-Means) to evaluate the data.
What does the main body of the work cover?
The main body covers data collection from Unani Medical College, the technical setup of WEKA, detailed comparative analysis of models, and the resulting classification of temperaments.
Which keywords define this work?
Key terms include Data Mining, WEKA, Temperament, Classification, Clustering, Association Rules, and NB Tree.
Why was the Naive Bayes model chosen over others?
The Naive Bayes model with training set data demonstrated minimum absolute mean error and higher True Positive rates compared to other models like J48.
How were the relationships between attributes identified?
Relationships were identified by applying the Apriory algorithm, which generated 10 best association rules showing significant connections between attributes like reaction speed and strength.
What were the findings regarding temperament classes?
The data revealed four temperament types: Bilious, Phlegmatic, Sanguine, and Melancholic, with a majority of instances belonging to the Bilious and Sanguine categories.
Was the EM clustering model successful?
No, the EM model was found to be ineffective as it clustered the data into only two types, which was deemed incorrect by the authors.
- Arbeit zitieren
- Dr. Bapurao Bandgar (Autor:in), Dr. Ajit D. More (Autor:in), 2020, Analysis of Temparament (Arab = Mizaj) by using different Data Mining Techniques, München, GRIN Verlag, https://www.grin.com/document/981618
