This document discusses classification and clustering techniques using the Weka data mining tool. It begins with an introduction to Weka and its capabilities for classification, clustering, and other data mining functions. It then provides an example of using Weka's J48 decision tree algorithm to classify iris flower samples based on sepal and petal attributes. Finally, it demonstrates k-means clustering on customer purchase data from a BMW dealership to group customers into five clusters based on their buying behaviors.
Weka project - Classification & Association Rule Generationrsathishwaran
The document discusses using the Weka data mining tool to analyze a US Congressional voting records dataset. It performs classification using 10-fold cross-validation, achieving 83.45% accuracy. It also generates association rules using the Apriori algorithm, setting a minimum support of 0.45 (196 instances) and minimum confidence of 0.9, resulting in 20 itemsets of size 1.
Classification and Clustering Analysis using Weka Ishan Awadhesh
This Term Paper demonstrates the classification and clustering analysis on Bank Data using Weka. Classification Analysis is used to determine whether a particular customer would purchase a Personal Equity PLan or not while Clustering Analysis is used to analyze the behavior of various customer segments.
WEKA is a machine learning software developed at the University of Waikato. It contains tools for data preprocessing, classification, regression, clustering, association rule mining, and attribute selection. It has graphical user interfaces like the Explorer for loading and preprocessing data, applying machine learning algorithms, and evaluating results. The Explorer allows users to classify and cluster data, find association rules, select important attributes, and visualize datasets and results. WEKA supports various algorithms for each task and can be used for educational and experimental purposes.
Weka is a collection of machine learning algorithms and data pre-processing tools developed at the University of Waikato. It contains tools for data pre-processing, classification, regression, clustering, association rule mining, and visualization. Weka is open source, free to use, and popular for research and applications. It has a graphical user interface and supports a variety of data formats including ARFF files.
This document discusses classification and clustering techniques using the Weka data mining tool. It begins with an introduction to Weka and its capabilities for classification, clustering, and other data mining functions. It then provides an example of using Weka's J48 decision tree algorithm to classify iris flower samples based on sepal and petal attributes. Finally, it demonstrates k-means clustering on customer purchase data from a BMW dealership to group customers into five clusters based on their buying behaviors.
Weka project - Classification & Association Rule Generationrsathishwaran
The document discusses using the Weka data mining tool to analyze a US Congressional voting records dataset. It performs classification using 10-fold cross-validation, achieving 83.45% accuracy. It also generates association rules using the Apriori algorithm, setting a minimum support of 0.45 (196 instances) and minimum confidence of 0.9, resulting in 20 itemsets of size 1.
Classification and Clustering Analysis using Weka Ishan Awadhesh
This Term Paper demonstrates the classification and clustering analysis on Bank Data using Weka. Classification Analysis is used to determine whether a particular customer would purchase a Personal Equity PLan or not while Clustering Analysis is used to analyze the behavior of various customer segments.
WEKA is a machine learning software developed at the University of Waikato. It contains tools for data preprocessing, classification, regression, clustering, association rule mining, and attribute selection. It has graphical user interfaces like the Explorer for loading and preprocessing data, applying machine learning algorithms, and evaluating results. The Explorer allows users to classify and cluster data, find association rules, select important attributes, and visualize datasets and results. WEKA supports various algorithms for each task and can be used for educational and experimental purposes.
Weka is a collection of machine learning algorithms and data pre-processing tools developed at the University of Waikato. It contains tools for data pre-processing, classification, regression, clustering, association rule mining, and visualization. Weka is open source, free to use, and popular for research and applications. It has a graphical user interface and supports a variety of data formats including ARFF files.
Tourist of holiday(ระบบผู้เชี่ยวชาญเพื่อช่วยตัดสินใจเลือกสถานที่ท่องเที่ยวสำห...KukKik Kf
This document describes an expert system created to help families choose tourist destinations in Thailand based on the season and their preferences and budget. It uses a decision tree and rule-based logic. The system asks the user questions about the season they want to visit, their budget, if they want beauty or not, and if they need accommodation. Based on the answers, it provides a recommendation of places like Phi Phi Island, waterfalls, temples, shopping malls, etc. It has rules defined to handle all combinations of answers. The interface then prompts the user to answer questions and provides a recommendation based on their answers.
Tourist of holiday(ระบบผู้เชี่ยวชาญเพื่อช่วยตัดสินใจเลือกสถานที่ท่องเที่ยวสำห...KukKik Kf
This document describes an expert system created to help families choose tourist destinations in Thailand based on the season and their preferences and budget. It uses a decision tree and rule-based logic. The system asks the user questions about the season they want to visit, their budget, if they want beauty or not, and if they need accommodation. Based on the answers, it provides a recommendation of top tourist destinations in Thailand that match their preferences.
Tourist of holiday(ระบบผู้เชี่ยวชาญเพื่อช่วยตัดสินใจเลือกสถานที่ท่องเที่ยวสำห...KukKik Kf
This document describes an expert system created to help families choose tourist destinations in Thailand based on the season and their preferences and budget. It uses a decision tree and rule-based logic. The system asks the user questions about the season they want to visit, their budget, if they want beauty or not, and if they need accommodation. Based on the answers, it provides a recommendation of places like Phi Phi Island, waterfalls, temples, shopping malls, etc. It has rules defined to handle all combinations of answers. The interface then prompts the user to answer questions and provides a recommendation based on their answers.
Tourist of holiday(ระบบผู้เชี่ยวชาญเพื่อช่วยตัดสินใจเลือกสถานที่ท่องเที่ยวสำห...KukKik Kf
This document describes an expert system created to help families choose tourist destinations in Thailand based on the season and their preferences and budget. It uses a decision tree and rule-based logic. The system asks the user questions about the season they want to visit, their budget, if they want beauty or not, and if they need accommodation. Based on the answers, it provides a recommendation of top tourist destinations in Thailand that match their preferences.
