Cluster analysis | Clustering criteria
Determining the number of clusters in a data set, a quantity often labelled k as in the k-means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem. For a certain class of clustering algorithms (in particular k-means, k-medoids and expectation–maximization algorithm), there is a parameter commonly referred to as k that specifies the number of clusters to detect. Other algorithms such as DBSCAN and OPTICS algorithm do not require the specification of this parameter; hierarchical clustering avoids the problem altogether. The correct choice of k is often ambiguous, with interpretations depending on the shape and scale of the distribution of points in a data set and the desired clustering resolution of the user. In addition, increasing k without penalty will always reduce the amount of error in the resulting clustering, to the extreme case of zero error if each data point is considered its own cluster (i.e., when k equals the number of data points, n). Intuitively then, the optimal choice of k will strike a balance between maximum compression of the data using a single cluster, and maximum accuracy by assigning each data point to its own cluster. If an appropriate value of k is not apparent from prior knowledge of the properties of the data set, it must be chosen somehow. There are several categories of methods for making this decision. (Wikipedia).
Determine Five-Number Summary, Outliers, and Create a Box Plot on (Even)
This video explains how to determine the five number summary, range, interquartile range, and outliers of a data set as well as create a box plot by hand. http://mathispower4u.com
From playlist Statistics: Describing Data
Determine Five-Number Summary, Outliers, and Create a Box Plot (Odd)
This video explains how to determine the five number summary, range, interquartile range, and outliers of a data set as well as create a box plot by hand. http://mathispower4u.com
From playlist Statistics: Describing Data
Ex: Determine a Five Number Summary (Even)
This video explains how to determine the five numbers summary of a data set. The method on determining the quartiles is the locator/percentile method. This is not the same as the TI84. http://mathispower4u.com
From playlist Statistics: Describing Data
Determine the Mean, Median, Mode, and Range of a Data Set
This video explains how to determine the mean, median, mode, and range of a data set. The result is check on the TI-84. http://mathispower4u.com
From playlist Statistics: Describing Data
Ex: Find the Mean and Median of a Data Set Given in a Frequency Table (odd)
This video explains how to determine the mean and median of a data set given in a frequency table. There is an odd number of data values. http://mathispower4u.com
From playlist Statistics: Describing Data
Determine How Many Subsets Meet Various Conditions (1)
This lesson provides examples of how to determine the number of subsets of a given set under various conditions.
From playlist Counting (Discrete Math)
Ex: Determine a Five Number Summary (Odd)
This video explains how to determine the five numbers summary of a data set. The method on determining the quartiles is the locator/percentile method. This is not the same as the TI84. http://mathispower4u.com
From playlist Statistics: Describing Data
How to find the five number summary for a set of ODD numbers. Finding min, max, median, Q1 and Q3 in simple steps.
From playlist Basic Statistics (Descriptive Statistics)
D2I - Matt Whithead discusses machine learning models in his Student Seminar
Ensemble machine learning models are often highly accurate on the supervised learning problem of classification. Combining groups of independent models allows for individual specialization and diversification with limited over fitting. The main drawback of using ensembles is the greatly in
From playlist Data to Insight Center (D2I)
Data Science with R | Data Science for Beginners | Introduction to Data Science | Edureka
** Data Science Master's Program: https://www.edureka.co/masters-program/data-scientist-certification ** This "Data Science with R" video by Edureka will help you to understand different Data Science concepts from scratch. The video starts with giving a brief introduction to data science f
From playlist Data Science Training Videos
K-Means Clustering - EXPLAINED!
This video is going to be divided into 3 parts: • High level intuition of what K-Means is, what it does and the algorithm. • K-means in math notation • Code an image compressor. Code for image compression: https://github.com/ajhalthor/kmeans-image-compression FOLLOW ME : https://www.quor
From playlist Algorithms and Concepts
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Simplilearn
K Means Clustering Algorithm tutorial video byb siomplilearn focuses on helping the aspiring machine learning enthusiats to have the fundamental knowledge if all the machine learning algorithms along with K Means Clustering Algorithm. This Machine learning tutorial focuses on K Means Clust
Clustering In Data Science | Data Science Tutorial | Simplilearn
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=Clustering-Data-Science-a3It88zzbiA&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/dat
From playlist Unsupervised Learning Algorithms [2022 Updated]
Applied Machine Learning 2019 - Lecture 15 - Clustering and Mixture models
K-Means, DBSCAN, hierarchical clustering, Gaussian Mixture Models Slides and materials on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
Methods for Measuring Distances Between Clusters
From playlist STAT 505