What is Association Analysis and Types of Frequent Patterns

Duration: 5 min

This video lesson is available to enrolled students.

Return to /learn/DSSSB-TGT-COMPUTER-SCIENCE-PAPER-2/data-base-management-system/data-warehousing-mining/data-mining-techniques/asset-what-is-association-analysis-and-types-of-frequent-patterns after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. 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++, Java Fundamentals, Array, String, IO & References, Language Design, Intro to Compilers, Visual Basics, Syntax Analysis: Bottom-Up, Object Oriented Technology
  3. 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, Cloud Computing, Mobile Computing, Net Layer:Routing Protocol, Cyber Safety and Security
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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)
  10. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  11. 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
  12. Design & Analysis of Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  13. Mathematics - I, II, III, IV: Permutation and Combination, Logarithm, Linear Algebra, 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, प्रतिशत / Percentage, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, अनुपात और समानुपात / Ratio and Proportion
  14. Computer Graphics, Multimedia & Mobile Computing: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing, Multimedia
  15. Emerging Trends: Emerging Tech Trends, Knowledge Representation, Natural Language Processing, व्यापार, विपणन एवं उपभोक्ता संरक्षण / Trade, Marketing and Consumer Protection
  16. Statistical Techniques: Probability, Statistics
  17. Interpolation - DI: आँकड़ा निर्वचन / Data Interpretation
  18. Basis of Physics: वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research
  19. Previous Year Paper:
  20. 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.

The video is a lecture on Association Analysis, a data mining technique. It begins by defining Association Analysis as the process of discovering frequent patterns, associations, and correlations within large datasets. The instructor explains that this involves two main steps: mining frequent patterns and then using them to find associations and correlations. The first part of the lecture focuses on the concept of a 'frequent pattern', which is a set of items that frequently appear together in a transactional dataset. This is illustrated with a diagram of a shopping cart and a table of transactions (T1, T2, T3, T4) showing itemsets like 'Bread, Milk' and 'Sugar, Jam'. The instructor then transitions to the different types of frequent patterns, introducing 'Frequent Itemsets' as a set of items that frequently co-occur. The lecture continues by defining 'Frequent Sequential Patterns' as patterns that occur in a specific order, such as a customer buying a PC before a memory card. Finally, the video introduces 'Frequent Structural Patterns', which are patterns that occur in different structural forms like graphs or trees, and are defined as a substructure that frequently appears. The presentation uses a whiteboard with handwritten notes and diagrams to explain these concepts.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video starts with a slide titled 'What is Association Analysis?'. The instructor explains that Association Analysis is about mining frequent patterns, which are patterns that occur frequently in a database. The slide shows a diagram of a shopping cart with items like milk, bread, and butter, with percentages (71%, 43%, 29%) indicating their frequency. The instructor writes 'Numerical' and 'Market Basket' on the board, and then writes 'Bread + Butter' and 'Bread + Juice' as examples of associations. The instructor also writes 'Frequent Pattern' and 'Frequent Item' to define the terms.

  2. 2:00 – 5:00 02:00-05:00

    The instructor continues to explain the concept of frequent patterns. The slide now shows a table with transaction IDs (T1, T2, T3, T4) and their corresponding item lists, such as 'Bread, Milk' and 'Sugar, Jam'. The instructor writes 'Frequent Itemsets' and defines it as a set of items that frequently appear together in transactional datasets, giving 'Milk & Bread' as an example. The instructor then moves to the next topic, 'What are types of Frequent Patterns?', and begins to define 'Frequent Sequential Pattern' as a pattern that occurs in a specific sequence, such as a customer buying a PC followed by a memory card. The instructor also writes 'Frequent Structural Pattern' and explains it as a substructure that frequently occurs in different forms like graphs or trees.

  3. 5:00 – 5:22 05:00-05:22

    The video transitions to a new slide titled 'What is Classification and Prediction?'. The instructor begins to explain this new topic, which is a different data mining task. The slide shows a diagram of a camera and a laptop, and the instructor writes 'Frequent' on the board. The instructor mentions that classification and prediction are used to predict the value of a target variable based on other variables. The instructor also writes '0.29 > E grade' as an example of a prediction.

The lecture provides a structured introduction to Association Analysis, a core data mining technique. It begins by defining the overall goal of discovering frequent patterns, associations, and correlations. The instructor then breaks down the concept into its fundamental components, starting with the definition of a 'frequent pattern' and illustrating it with a shopping cart example and a transactional table. The lesson progresses by categorizing the different types of frequent patterns: 'Frequent Itemsets' for co-occurring items, 'Frequent Sequential Patterns' for ordered events, and 'Frequent Structural Patterns' for recurring substructures. This logical progression from a general definition to specific types provides a clear framework for understanding how association analysis works in practice.

Loading lesson…