Free Download Feature and Dimensionality Reduction for Clustering with Deep Learning
English | 2024 | ISBN: 3031487427 | 460 Pages | PDF EPUB (True) | 34 MB
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by "family" to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.

Scrucca L Model-Based Clustering, Classification, Using mclust in R 2023 | 28.26 MB
English | 269 Pages

Title: Model-Based Clustering, Classification, and Density Estimation Using mclust in R
Author: Luca Scrucca
Year: 2019

Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.
Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.
All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.
Duration: 3h 22m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 532 MB

Unsupervised Machine Learning for Clustering in Political and Social Research
English | 2020 | ISBN: 110879338X | 70 Pages | PDF (True) | 10 MB
In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications by Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
English | PDF | 2008 | 253 Pages | ISBN : 3540787364 | 5.1 MB
The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means.

Astronomy and Big data: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology by Kieran Jay Edwards
English | PDF | 2014 | 112 Pages | ISBN : 331906598X | 6.7 MB
With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as "Uncertain".

English | 2022 | ISBN: 9811241198 | 882 pages | True PDF EPUB | 83.05 MB
This unique compendium gives an updated presentation of clustering, one of the most challenging tasks in machine learning. The book provides a unitary presentation of classical and contemporary algorithms ranging from partitional and hierarchical clustering up to density-based clustering, clustering of categorical data, and spectral clustering.Most of the mathematical background is provided in appendices, highlighting algebraic and complexity theory, in order to make this volume as self-contained as possible. A substantial number of exercises and supplements makes this a useful reference textbook for researchers and students.

Released 05/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 49m | Size: 127.7 MB

Pragmatic Ai - K-means Clustering Theory Algorithm Implementation and Scaling-iLLiTERATE
English | Size: 502.99 MB
Category: Tutorial

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 55 lectures (3h 10m) | Size: 2.85 GB
Become an expert and solve Real World Problems using Clustering Analysis and Python