A K-D heap is a data structure in computer science which implements a multidimensional priority queue without requiring additional space. It is a generalization of the Heap. It allows for efficient insertion, query of the minimum element, and deletion of the minimum element in any of the k dimensions, and therefore includes the double-ended heap as a special case. (Wikipedia).
Dynamic Memory Allocation In C | C Memory Management Explained | C For Beginners | Simplilearn
"This video by Simplilearn will explain to you Heap Data Structure Tutorial. Heap Data Structure Tutorial For Beginners will explain the types of heap data structures in c. heap insertion and deletion operation with examples. This C programming tutorial will cover theoretical and practical
From playlist C++ Tutorial Videos
Heap Sort - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Clustering 3: overview of methods
Full lecture: http://bit.ly/K-means In this course we cover 4 different clustering algorithms: K-D trees (part of lecture 9), K-means (this lecture), Gaussian mixture models (lecture 17) and agglomerative clustering (lecture 20).
From playlist K-means Clustering
Understanding Java Garbage Collector and Heap | Java Garbage Collection Tutorial | Edureka
( Java Training - https://www.edureka.co/java-j2ee-training-course ) The video mainly explains heap and garbage collector. Heap is the memory area in JVM where objects are created. Garbage Collector is a special thread, which clears the memory area during runtime, freeing it from the objec
From playlist Java Tutorial For Beginners | Edureka
Heap Sort Performance Solution - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
H U K is a subspace iff H is contained in K or K is contained in H Proof
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys H U K is a subspace iff H is contained in K or K is contained in H Proof.
From playlist Proofs
Clustering in Python - K-means, Hierarchical Clustering & DBSCAN
The code can be accessed at https://github.com/sepinouda/Machine-Learning/tree/main/Code%20-%20Lecture%206
From playlist Machine Learning Course
12. Parallel Storage Allocation
MIT 6.172 Performance Engineering of Software Systems, Fall 2018 Instructor: Julian Shun View the complete course: https://ocw.mit.edu/6-172F18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63VIBQVWguXxZZi0566y7Wf Prof. Shun discusses the differences between malloc()
From playlist MIT 6.172 Performance Engineering of Software Systems, Fall 2018
Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer
Learn and master the most common data structures in this full course from Google engineer William Fiset. This course teaches data structures to beginners using high quality animations to represent the data structures visually. You will learn how to code various data structures together wi
From playlist Java Tutorials
Xavier Viennot: Heaps and lattice paths
CIRM HYBRID EVENT Recorded during the meeting "Lattice Paths, Combinatorics and Interactions" the June 25, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Combinatorics
MIT 6.172 Performance Engineering of Software Systems, Fall 2018 Instructor: Julian Shun View the complete course: https://ocw.mit.edu/6-172F18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63VIBQVWguXxZZi0566y7Wf This lecture discusses different means of storage allo
From playlist MIT 6.172 Performance Engineering of Software Systems, Fall 2018
MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This class discusses a fourth weighted single-source shortest path algor
From playlist MIT 6.006 Introduction to Algorithms, Spring 2020
Fibonacci Heaps or "How to invent an extremely clever data structure"
I want to tell you about a daunting, but truly fascinating data structure. At first sight, Fibonacci Heaps can seem intimidating. In this video, I'm going to show you all the necessary steps to invent a really clever data structure. 00:00 Introduction 00:50 Priority Queues and Binary Heap
From playlist Advanced Algorithms/Data Structures
Recitation 7: Comparison Sort, Counting and Radix Sort
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Victor Costan License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.006 Introduction to Algorithms, Fall 2011
Heap Sort Algorithm | Heap Sort In Data Structure | Heap Sort With Example | Simplilearn
This video is based on Heap sort Algorithm. This heap sort in data structures tutorial makes sure that the heap sort algorithm is explained well and will help the beginners understand the basics of heap sort with examples. The video also covers practical demo for a better learning experien
From playlist Data Structures & Algorithms
RubyConf 2016 - Methods of Memory Management in MRI by Aaron Patterson
RubyConf 2016 - Methods of Memory Management in MRI by Aaron Patterson Let's talk about MRI's GC! In this talk we will cover memory management algorithms in MRI. We will cover how objects are allocated and how they are freed. We will start by looking at Ruby's memory layout, including pag
From playlist RubyConf 2016
Clustering (3): K-Means Clustering
The K-Means clustering algorithm. Includes derivation as coordinate descent on a squared error cost function, some initialization techniques, and using a complexity penalty to determine the number of clusters.
From playlist cs273a
Indexing 9: doc-at-a-time worst case
A naive implementation of the doc-at-a-time strategy can be quadratic in the total number of entries in the lists we're merging. This worst-case scenario arises when we have many short lists. A workaround is to implement a min-heap (a priority queue) for the current nodes across the lists
From playlist IR7 Inverted Indexing