BranchesTags

Latest commit

History

1 Commit

Folders and files

NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md

Repository files navigation

Big Data Analytics

Implementation of Some of the Big Data Analytics Algorithms in Python

#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".

Author

Rabist - view on LinkedIn

Details

  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022

License

Licensed under MIT.

About

Implementation of Big Data Analytics Algorithms in Python

Topics

amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Latest commit
History
1 Commit
Folders and files
NameName
Last commit message
Last commit date
final-project
final-project
hw1
hw1
hw2
hw2
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Big Data Analytics
Implementation of Some of the Big Data Analytics Algorithms in Python
#TitleDescription
1Friendship RecommendationSuggest new friends to individual users based on their mutual friends using PySpark.
2Association RulesImplementation of A-priori algorithm for frequent item set mining and association rule learning.
3Locality-sensitive HashingImplementation of LSH algorithmic technique that hashes similar input items into the same buckets with high probability.
4DGIM AlgorithmDGIM algorithm implementation to find the number 1's in a dataset.
5Recommender SystemItem-based and user-based collaborative filtering using PySpark.
6k-meansk-means clustering algorithm.
7Triangle CountingImplementations of the algorithms for the adjacency list model in the experiments in the paper "Triangle and Four Cycle Counting with Predictions in Graph Streams".
Author
Rabist - view on LinkedIn
Details
  • Course: Advanced Topics (Big Data Analytics) - MS
  • Teacher:Dr. Mostafa HaghirChehreghani
  • Univ: Amirkabir University of Technology
  • Semester: Spring 2022
License
Licensed under MIT.
About
Implementation of Big Data Analytics Algorithms in Python
Topics
amirkabir-universityassociation-rulesbig-databig-data-analyticsbigdatacollaborative-filteringcs246data-miningdata-sciencefrequent-itemset-miningfriendship-algorithmgerakedgraphkmeans-clusteringlocality-sensitive-hashingrabistrecommender-systemstanford-coursestream-processingtriangle-counting
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages