About this course: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

Yearly citation counts for the publication titled "Data structures and algorithms for disjoint set union problems".

Hello everyone,

I am excited to share with you a comprehensive list of algorithms that could be useful for anyone who wants to learn or refresh their knowledge. This list includes all the necessary algorithms you need to know.

Please feel free to share it with others and consider supporting me if you find it helpful ⭐️.

Question Paper Solutions of chapter Non-linear Data structures: Trees of Data Structure and Algorithm, 4th Semester , Applied Electronics and Instrumentation Engineering

Question Paper Solutions of chapter Non-linear Data structures: Graphs of Data Structure and Algorithm, 4th Semester , Applied Electronics and Instrumentation Engineering

This bundle contains most of the courses available in Data Structure and Algorithms by Algoexpert as well as Neetcode.These contains a curated list of algorithms and problems that are found in Leetcode.

Yearly citation counts for the publication titled "Data Structures".

Question Paper Solutions of chapter Sorting and Hashing of Data Structure and Algorithm, 5th Semester , Electrical Engineering

Yearly citation counts for the publication titled "Abstractions, algorithms and data structures for structural bioinformatics inPyCogent".

Network of 42 papers and 87 citation links related to "Data structures and algorithms for disjoint set union problems".

Question Paper Solutions of Data Structure and Algorithm (OE-EE-501A),5th Semester,Electrical Engineering,Maulana Abul Kalam Azad University of Technology

We propose a new data structure multiset-trie that is designed for storing and efficiently processing a set of multisets. Moreover, multiset-trie can operate on a set of sets without efficiency loss. The multiset-trie is a search tree with properties similar to those of a trie. It implements all standard search tree operations together with the multiset containment operations such as sub-multiset and super-multiset. Suppose we have a set of multisets S and a multiset X. The multiset containment operations retrieve multisets from S that are either sub-multisets or super-multisets of X. We present the mathematical analysis of a multiset-trie that gives the time complexity of the algorithms and the space complexity of the data structure. Further, the empirical analysis of the data structure is implemented in a series of experiments. The experiments illuminate the time complexity space of the multiset containment operations. For reproducability reasons we publish the datasets used in our experiments, in this repository.

Question Paper Solutions of chapter Arrays of Data Structure and Algorithm, 3rd Semester , Bachelor of Computer Application 2020-2021

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Big data, with N × P dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, such as K-means or hierarchical clustering are popular methods for performing exploratory analysis on large datasets. Unfortunately, these methods are not always possible to apply to big data due to memory or time constraints generated by calculations of order P*N(N−1)2. To circumvent this problem, typically the clustering technique is applied to a random sample drawn from the dataset; however, a weakness is that the structure of the dataset, particularly at the edges, is not necessarily maintained. We propose a new solution through the concept of “data nuggets”, which reduces a large dataset into a small collection of nuggets of data, each containing a center, weight, and scale parameter. The data nuggets are then input into algorithms that compute methods such as principal components analysis and clustering in a more computationally efficient manner. We show the consistency of the data nuggets based covariance estimator and apply the methodology of data nuggets to perform exploratory analysis of a flow cytometry dataset containing over one million observations using PCA and K-means clustering for weighted observations. Supplementary materials for this article are available online.

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This paper provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM).

My Notes 📓

This repository contains my lecture notes from graduate school on following topics 👇🏼

Data Science: 8 cheatsheets Machine Learning (follows Tom Mitchell's book): 25 pages of notes Statistics: 9 cheatsheets Deep Learning: 12 cheatsheets, will upload more Image Processing (follows digital image processing book): 21 cheatsheets Data Structures and Algorithms (follows this book by Goodrich): 26 cheatsheets

✨ Some notes ✨

Most of these notes aren't intended to teach a… See the full description on the dataset page: https://huggingface.co/datasets/merve/my_notes.

Question Paper Solutions of chapter Trees of Data Structure and Algorithm, 3rd Semester , Information Technology

The data is collected from students in the Department of Computer Science at the VNU-HCM University of Technology (Vietnam) in the 2023 and 2024 academic years. We collect data in two courses, Data Structure and Algorithm (DSA, Fall semester) and Programming Fundamental (PF, Spring semester). Most students are in Programming Fundamentals in their first year and Data Structure in their second year. DSA has PF as a prerequisite, and PF has Introduction to Computing as a prerequisite. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/stair-lab/code_insights_csv.