Big Data Analytics - Module 2 - UNIT II - CLUSTERING AND CLASSIFICATION . . . Advanced Analytical Theory and Methods: Overview of Clustering - K-means - Use Cases - Overview of the Method - Determining the Number of Clusters - Diagnostics - Reasons to Choose and Cautions - Classification: Decision Trees - Overview of a Decision Tree - The General Algorithm - Decision Tree Algorithms - Evaluating a Decision Tree - Decision Trees in R - NaÔve Bayes - Bayesë Theorem - NaÔve Bayes Classifier
Big Data Analytics - Archive. org • Clustering –Unsupervised learning method • K-means clustering: • Use cases • The algorithm • Determining the optimum value for K • Diagnostics to evaluate the effectiveness of the method • Reasons to Choose (+) and Cautions (-) of the method part 1: K-means Clustering Module 4: Analytics Theory Methods 9
CS8091 - Big Data Analytics - Regulation 2017 Syllabus - STUCOR UNIT II CLUSTERING AND CLASSIFICATION Advanced Analytical Theory and Methods: Overview of Clustering — K-means — Use Cases — Overview of the Method — Determining the Number of Clusters — Diagnostics — Reasons to Choose and Cautions - Classification: Decision Trees — Overview of a Decision Tree — The General Algorithm — Decision Tree Algorithms — Evaluating a Decision Tree — Decision Trees in R — Naïve Bayes — Bayes? Theorem — Naïve Bayes Classifier
K means Clustering – Introduction - GeeksforGeeks K means Clustering The algorithm will categorize the items into k groups or clusters of similarity Selecting the right number of clusters is important for meaningful segmentation to do this we use Elbow Method for optimal value of k in KMeans which is a graphical tool used to determine the optimal number of clusters (k) in K-means Implementation of K-Means Clustering in Python We will use blobs datasets and show how clusters are made
Data Science and Big Data Analytics Chap 4: Advanced Analytical Theory . . . 4 2 K-means Algorithm Given a collection of objects each with n measurable attributes and a chosen value k of the number of clusters, the algorithm identifies the k clusters of objects based on the objects proximity to the centers of the k groups The algorithm is iterative with the centers adjusted to the mean of each cluster’s n- dimensional vector of attributes
Module 4 Advanced Analytics - Theory and Methods • Clustering –Unsupervised learning method • K-means clustering: • Use cases • The algorithm • Determining the optimum value for K • Diagnostics to evaluate the effectiveness of the method • Reasons to Choose (+) and Cautions (-) of the method Lesson 1: K-means Clustering Module 4: Analytics Theory Methods 8
Advanced Analytical Theory and Methods: Clustering Building upon the introduction to R presented in Chapter 3, “Review of Basic Data Analytic Methods Using R,” Chapter 4, “Advanced Analytical Theory and Methods: Clustering” through Chapter 9, “Advanced Analytical Theory and Methods: Text Analysis” describe several commonly used analytical methods that may be considered for the Model Planning and Execution phases (Phases 3 and 4) of the Data Analytics Lifecycle This chapter considers clustering techniques and algorithms 4 1
BDA - Unit 2 - Notes - UNIT 2 - CS8091 - BIG DATA ANALYTICS - Studocu CS8091 - BIG DATA ANALYTICS – Unit II – Lecture Notes UNIT II - CLUSTERING AND CLASSIFICATION Advanced Analytical Theory and Methods: Overview of Clustering - K-means - Use Cases - Overview of the Method - Determining the Number of Clusters - Diagnostics - Reasons to Choose and Cautions - Classification: Decision Trees - Overview of a Decision Tree - The General Algorithm - Decision Tree Algorithms - Evaluating a Decision Tree - Decision Trees in R - Naïve Bayes - Bayes‘ Theorem
CS8091-BIG DATA ANALYTICS - msajce-edu. in Advanced Analytical Theory and Methods: Overview of Clustering – K-means – Use Cases – Overview of the Method – Determining the Number of Clusters – Diagnostics – Reasons to Choose and Cautions - Classification: Decision Trees – Overview of a Decision Tree – The General Evaluating a Decision Tree – Decision Trees in R – Naïve Bayes – Bayes‘ Theorem – Naïve Bayes Classifier UNIT III ASSOCIATION AND RECOMMENDATION SYSTEM CS8091 Syllabus Big Data Analytics
Advanced Analytics: Theory and Methods | K-means Clustering, ADVANCED ANALYTICS: THEORY AND METHODS 2 find highly significant associations with less frequent items, while lever may prefer items that have a higher frequency support in the dataset Support is an indication of how frequently the items appear in the data Confidence indicates the number of times the if-then statements are found true A third metric, called lift, can be used to compare confidence with expected confidence, or how many times an if-then statement is expected to be found true