6 Repos
Awesome GitHub RepositoriesAnagram Grouping Algorithms
Algorithms for clustering strings into groups based on shared character frequencies.
Distinct from Anagram Substring Identifiers: Focuses on clustering whole strings into groups, unlike substring identification.
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Awesome Anagram Grouping Algorithms GitHub Repositories
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- azl397985856/leetcode
azl397985856/leetcode
55,758Auf GitHub ansehenThis project is a curated educational resource and solution repository for algorithmic challenges, specifically focused on LeetCode problems. It serves as a technical reference for common data structures and algorithmic patterns, providing verified code implementations across multiple programming languages alongside detailed logic and complexity analysis. The repository functions as a comprehensive study guide for competitive programming and technical interview preparation. It includes specialized learning tools such as an Anki flashcard dataset for spaced repetition and a browser extension t
Implements anagram grouping by sorting or counting character frequencies to create unique clustering keys.
JavaScriptalgoalgorithmalgorithms - apachecn/apachecn-algo-zh
apachecn/apachecn-algo-zh
11,140Auf GitHub ansehenThis project is an algorithm study resource, a centralized LeetCode solution repository, and a technical interview study guide. It provides Chinese translations of textbooks and guides on data structures and algorithms for academic study and professional preparation. The project distinguishes itself by delivering multi-language solution repositories and translated academic materials through a static site generation model. This architecture enables compile-time content translation and offline-first delivery of educational assets as static files. The repository covers a wide range of algorithm
Implements algorithms to group strings into anagram sets using character frequency keys.
JavaScriptalgorithmcodingdata-structure - apachecn/interview
apachecn/Interview
8,944Auf GitHub ansehenThis project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie
Implements algorithms for identifying and grouping strings based on shared character frequencies.
Jupyter Notebookinterviewkaggleleetcode - sharingsource/logicstack-leetcode
SharingSource/LogicStack-LeetCode
7,495Auf GitHub ansehenLogicStack-LeetCode is a curated repository of solved algorithm problems and data structure implementations, primarily drawn from the LeetCode platform. Its core identity is a structured collection of solutions designed to support technical interview preparation and competitive programming practice, with each solution accompanied by complexity analyses to help engineers understand performance trade-offs. The repository distinguishes itself through its breadth of coverage across fundamental algorithmic patterns and data structures. It includes implementations for array manipulation, string pro
Provides algorithms for clustering strings into groups based on shared character frequencies.
algorithminterview-practiceinterview-questions - soapyigu/leetcode-swift
soapyigu/LeetCode-Swift
4,958Auf GitHub ansehenLeetCode-Swift is a collection of algorithm solutions written in Swift, designed for coding interview preparation. Each solution is implemented as a self-contained function with no external dependencies, making it easy to run and test. The repository organizes solutions by topic and company, and every file includes time and space complexity annotations, allowing quick evaluation of algorithmic efficiency. What sets this repository apart is its flat file structure and the way solutions are tagged with the companies that asked them in interviews, enabling targeted practice. All code resides in
Groups strings into subarrays where each subarray contains words that are anagrams, using sorted strings as dictionary keys.
Swiftalgorithmdata-structuresinterview - marcosfede/algorithms
marcosfede/algorithms
1,132Auf GitHub ansehenThis project is a comprehensive repository of fundamental computer science algorithms and data structures designed as a reference for academic study, technical interview preparation, and competitive programming. It provides standardized implementations of core computational strategies, serving as an educational resource for developers to master software engineering fundamentals and algorithmic problem-solving. The collection distinguishes itself through a multi-language approach, offering cross-language solutions for complex tasks ranging from graph traversal and dynamic programming to bitwis
Groups strings into categories based on shared patterns like anagrams.
Pythonalgorithmbfscompetitive-programming
