Apriori Algo

Duration: 17 min

This video lesson is available to enrolled students.

Return to /learn/DSSSB-TGT-COMPUTER-SCIENCE-COMPLETE-COURSE/data-base-management-system/data-warehousing-mining/advanced-data-mining-methods/asset-apriori-algo after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. Section A: General Awareness: Polity, वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research, Biology, Chemistry, Static General Knowledge, History, Geography, Indian Economy, समसामयिकी / Current Affairs, Evolution of Indian Banking (Banking & Financial Awareness), Environment
  3. Section A: General Intelligence & Reasoning ability: श्रेणी / Series, कूट लेखन एवं कूट वाचन / Coding-Decoding, Data Sufficiency, अशाब्दिक तर्क / Non-Verbal Reasoning, Analytical Reasoning (Counting Figures Reasoning), रक्त संबंध / Blood Relations, Syllogisms, कथन आधारित तार्किक विवेचन / Statement-Based Reasoning, बैठक व्यवस्था एवं पहेलियाँ / Seating Arrangement and Puzzles, दिशा एवं दूरी / Direction and Distance, गणितीय संक्रियाएँ एवं लुप्त संख्या / Mathematical Operations and Missing Numbers, कैलेंडर एवं घड़ी / Calendar and Clock, Clock, घन, पासा एवं आकृतियों की गिनती / Cubes, Dice and Figure Counting, आँकड़ों की पर्याप्तता एवं निर्णय क्षमता / Data Sufficiency and Decision Making, वर्णमाला एवं संख्या परीक्षण / Alphabet and Number Test, Crypt Arithmetic, वेन आरेख, न्याय वाक्य एवं असमानता / Venn Diagrams, Syllogism and Inequalities, Data Arrangement, Inequality, सादृश्यता एवं वर्गीकरण / Analogy and Classification, क्रम एवं रैंकिंग / Order and Ranking, TCS NQT Advanced Reasoning Ability
  4. Section A: Arithmetical & Numerical Ability: अनुपात और समानुपात / Ratio and Proportion, Divisibility Rules, आँकड़ा निर्वचन / Data Interpretation, मिश्रण और एलिगेशन / Mixture and Alligation, Permutation and Combination, Logarithm, संख्या पद्धति / Number System, महत्तम समापवर्तक और लघुत्तम समापवर्त्य / HCF and LCM, अनुक्रम, श्रेणी और संयोजन / Sequences, Series and Combinatorics, समय, चाल और दूरी / Time, Speed and Distance, प्रतिशत / Percentage, औसत / Average, लाभ, हानि और बट्टा / Profit, Loss and Discount, साधारण और चक्रवृद्धि ब्याज / Simple and Compound Interest, समय और कार्य / Time and Work, Pipe and Cistern, घन क्षेत्रमिति / Solid Mensuration, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, Algebra, Probability, Mental Ability, Age Problems, Partnership, Statistics, Arithmetic, Speed Mathematics and Basic Mathematics, Miscellaneous
  5. Section A: English Language & Comprehension: Vocabulary, Subject Verb Agreement (Verb Noun Agreement), Adjectives, Verbs and Tenses, Reading Comprehension, Parts of Speech, Sentence Construction, Prepositions and Conjunctions, Direct and Indirect Speech, Sentence Completion (Fill in the blanks), Sentence Structure and Common Errors, Modals, Transformation of Sentences, Sentence Correction (Error Correction), Adverb, Conjunction, Cloze Test (Cloze Passage)
  6. Section A: Hindi Language & Comprehension: भाषा, लिपि और वर्ण, वर्तनी, वर्ण-विन्यास, अनुस्वार, अनुनासिक, चंद्रबिंदु और ‘र’ के रूप, शब्द, पद और शब्द-भेद, संज्ञा और सर्वनाम, पद-भेद और रूप-विकार, लिंग, वचन, पुरुष और कारक, काल, वाच्य, वाक्य और वाक्य-भेद, संधि, समास, विलोम शब्द, पर्यायवाची शब्द, सामान्य अशुद्धियाँ, अनेक शब्दों के लिए एक शब्द, शब्द-भंडार, अपठित बोध, Multi-Topic Content (Cross-Concept Material), वर्णमाला
  7. Section B: Programming, Data Structures, OOP & Java: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Java, OOPS with C++, Language Design, Intro to Compilers, Visual Basics, Syntax Analysis: Bottom-Up, Object Oriented Technology
  8. Section B: Computer Networks, TCP/IP & Network Security: Introduction to CN, Data Communication, DLL: Flow Control, DLL: Access Control, DLL: Error Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Access Technologies, Network Security, Society, Law & Ethics, Mobile Computing, Net Layer:Routing Protocol, Cyber Safety and Security
  9. Section B: Teaching Methodology: वृद्धि और विकास / Growth and Development, Concept of Pedagogy, विकास के सिद्धांत / Theories of Development, समावेशी शिक्षा / Inclusive Education, Experiential Pedagogy, आकलन और मूल्यांकन / Assessment and Evaluation, समाजीकरण और जेंडर / Socialisation and Gender, National Education Policy (NEP) 2020, संज्ञानात्मक प्रक्रियाएँ / Cognitive Processes, शिक्षण योजना और शिक्षण कौशल / Teaching Planning and Teaching Skills, Personality Development, शिक्षण विधियाँ / Teaching Methods, अधिगम और व्यवहारवादी सिद्धांत / Learning and Behavioural Theories, Learners & Learning Process, Factors Affecting Teaching, शिक्षण विधियाँ एवं उपागम / Teaching Methods and Approaches, शिक्षण-अधिगम प्रक्रिया एवं कक्षा प्रबंधन / Teaching-Learning Process and Classroom Management, शिक्षण सहायक सामग्री एवं शैक्षिक तकनीकी / Teaching Aids and Educational Technology, सूक्ष्म शिक्षण एवं कक्षा संप्रेषण / Microteaching and Classroom Communication, आकलन एवं मूल्यांकन / Assessment and Evaluation, पाठ्यचर्या, कला एवं सह-शैक्षिक क्षेत्र / Curriculum, Arts and Co-curricular Areas, दिव्यांग बच्चों की शिक्षा / Education of Children with Disabilities, शिक्षक: गुण, व्यावसायिक नैतिकता एवं विकास / Teacher: Qualities, Professional Ethics and Development
  10. Section B: Digital Electronics / Boolean Logic: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Analog-Digital Conversion
  11. Section B: Operating Systems & Linux: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, File Management, Disc Scheduling, Unix OS, Linux OS, Distributed Systems, Windows OS
  12. Section B: Data Base Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, Relational Algebra, SQL, File Organization & Indexing, Transaction Management, Concurrency Control, Database Security & Authorization, Database Recovery, Data Warehousing & Mining
  13. Section B: Internet & Web Technologies: Website, Browser & Services, Internet Fundamentals, Web Design & Development, Protocols & Services, Web Publishing & Hosting, Server Side & Security, HTML Basics, CSS, Emerging Technologies, JavaScript-Basics, .Net Programming, Electronic Data Interchange and E-Commerce, XML
  14. Section B: Computer Fundamentals, IT Basics & Architecture: Basics of COA, Cache Memory Organization, Input Output Organisation, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer, Terms & Abbreviations, MS Office Applications, Instr Formats & Modes, Floating Point Rep, कंप्यूटर के मूल सिद्धांत / Computer Fundamentals, साइबर सुरक्षा / Cyber Safety and Security, कंप्यूटर सॉफ़्टवेयर / Computer Software, ऑपरेटिंग सिस्टम / Operating System, वर्ड प्रोसेसिंग (MS Word) / Word Processing (MS Word), कंप्यूटर नेटवर्क / Computer Networks, प्रेज़ेंटेशन (MS PowerPoint) / Presentation Software (MS PowerPoint)
  15. Section B: Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  16. Section B: Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, extras, Fundamentals of Systems and Information Systems
  17. Section B: Design & Analysis of Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  18. Section B: Mathematics: Linear Algebra, Calculus, Trigonometry, Vectors, Set Theory, Functions, Relations, Propositional and Predicate Logic, Research Process Steps, Graph Theory, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, संख्या पद्धति और संख्या सिद्धांत / Number Systems and Number Theory, यूक्लिडीय ज्यामिति और क्षेत्रमिति / Euclidean Geometry and Mensuration, सांख्यिकी और प्रायिकता / Statistics and Probability, निर्देशांक ज्यामिति, सदिश और त्रिविमीय ज्यामिति / Coordinate Geometry, Vectors and 3D Geometry, बहुपद, समीकरण और सम्मिश्र संख्याएँ / Polynomials, Equations and Complex Numbers, समतल क्षेत्रमिति / Plane Mensuration, प्रतिचयन / Sampling
  19. Section B: Computer Graphics, Multimedia & Mobile Computing: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing, Multimedia
  20. Section B: Emerging Trends: Emerging Tech Trends, Knowledge Representation, Natural Language Processing, व्यापार, विपणन एवं उपभोक्ता संरक्षण / Trade, Marketing and Consumer Protection
  21. DSSSB Previous Year Paper:
  22. Business Studies and Management: Business Communication, प्रबंध के सिद्धांत एवं कार्य / Principles and Functions of Management, लेखांकन के आधार एवं लेन-देनों का अभिलेखन / Accounting Fundamentals and Recording of Transactions, Business Economics, ह्रास, अंतिम खाते व कंप्यूटरीकृत लेखांकन / Depreciation, Final Accounts and Computerised Accounting, Knowledge Management & New Economy
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video provides a comprehensive overview of the Apriori algorithm, a foundational method for mining frequent itemsets and generating association rules in transactional databases. The lecture begins by defining the algorithm and its core concepts, including frequent itemsets, the Apriori property, and the join operation. It then transitions into a detailed, step-by-step example using a database of 9 transactions. The process is broken down into five main steps: 1) Generating 1-itemset frequent patterns by scanning the database and filtering items based on a minimum support count of 2. 2) Generating 2-itemset frequent patterns by joining the 1-itemset frequent patterns and counting their support. 3) Generating 3-itemset frequent patterns using the same join and count process. 4) Pruning candidate itemsets using the Apriori property to eliminate non-frequent subsets. 5) Generating association rules from the final frequent itemsets and selecting those with a confidence above a minimum threshold of 70%. The video uses a combination of on-screen text, diagrams, and handwritten annotations to clearly illustrate each stage of the algorithm.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video opens with a slide titled "The Apriori Algorithm: Basics". The instructor introduces the Apriori Algorithm as a method for mining frequent itemsets for boolean association rules. Key concepts are defined: Frequent Itemsets are sets of items with a minimum support count, denoted as L_i for the i-th itemset. The Apriori Property states that any subset of a frequent itemset must also be frequent. The Join Operation is described as the process of generating candidate k-itemsets (L_k) by joining L_{k-1} with itself. The instructor highlights the term "Apriori Algorithm" and the phrase "frequent itemsets" on the slide.

  2. 2:00 – 5:00 02:00-05:00

    The presentation transitions to a practical example. A slide titled "The Apriori Algorithm in a Nutshell" is shown, followed by a slide with a database D of 9 transactions. The instructor explains the problem: find frequent itemsets using the Apriori algorithm with a minimum support count of 2 (2/9 = 22%). The minimum confidence required is set to 70%. The instructor then begins the first step, calculating the support count for each individual item (I1, I2, I3, I4, I5) by scanning the database. The support counts are I1:6, I2:7, I3:7, I4:6, I5:2. The instructor writes these counts on the board, noting that I5 has a support count of 2, which is equal to the minimum support, so it is included in the first frequent itemset L1.

  3. 5:00 – 10:00 05:00-10:00

    The video details Step 1: Generating 1-itemset Frequent Pattern. A diagram shows the process of scanning the database (D) to count the support of each candidate 1-itemset (C1). The support counts are listed, and the algorithm compares them to the minimum support count of 2. The resulting set of frequent 1-itemsets, L1, is {I1, I2, I3, I4}. The instructor then moves to Step 2: Generating 2-itemset Frequent Pattern. The process involves generating candidate 2-itemsets (C2) by joining L1 with itself. The diagram shows the generation of candidates like [I1,I2], [I1,I3], etc. The support count for each candidate is then calculated by scanning the database. For example, the support count for [I1,I2] is 4. The candidates with support count less than 2 are pruned, resulting in L2.

  4. 10:00 – 15:00 10:00-15:00

    The video continues with Step 3: Generating 3-itemset Frequent Pattern. The instructor explains that the candidate 3-itemsets (C3) are generated by joining L2 with itself, using the Apriori property to prune candidates. The diagram shows the generation of candidates like [I1,I2,I3] and [I1,I2,I5]. The support count for each candidate is then calculated. For instance, the support count for [I1,I2,I3] is 2. The instructor notes that the support count for [I1,I2,I5] is 2, which is equal to the minimum support, so it is included in the final frequent itemset L3. The video then transitions to Step 5: Generating Association Rules from Frequent Itemsets. The instructor explains that for each frequent itemset, all possible association rules are generated and their confidence is calculated. Rules with confidence below the minimum threshold of 70% are rejected.

  5. 15:00 – 16:41 15:00-16:41

    The final segment focuses on Step 5: Generating Association Rules. The instructor provides a worked example for the frequent itemset {I1, I2, I5}. Three rules are generated: R4: I1 → I2 ^ I5, R5: I2 → I1 ^ I5, and R6: I5 → I1 ^ I2. The confidence for each rule is calculated. For R4, confidence = support(I1,I2,I5) / support(I1) = 2/6 = 33%, which is less than 70%, so it is rejected. For R5, confidence = 2/7 = 29%, which is also rejected. For R6, confidence = 2/2 = 100%, which is above 70%, so it is selected. The instructor concludes that three strong association rules have been found, summarizing the entire process.

The video provides a clear, structured, and practical demonstration of the Apriori algorithm. It effectively bridges the gap between theoretical concepts and real-world application by walking through a complete, step-by-step example. The use of a consistent, small dataset allows the viewer to follow each iteration of the algorithm, from the initial scan for 1-itemsets to the final generation of association rules. The instructor's methodical approach, combining on-screen text with handwritten annotations, reinforces the key principles of the algorithm, particularly the importance of the Apriori property in pruning the search space and the process of calculating support and confidence to derive meaningful rules.

Loading lesson…