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Data mining involves analyzing large datasets to discover patterns and relationships. It has various applications across different types of data, including structured databases, text, images, and sensor data. Major techniques used in data mining include classification, clustering, association rule mining, and prediction. These techniques are used to gain insights from data to solve problems in diverse domains like business, science, healthcare, and more.
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* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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dwm.pptx
- 2. Unit 1 • Why Data Mining? • What Is Data Mining? • A Multi-Dimensional View of Data Mining • What Kind of Data Can Be Mined? • What Kinds of Patterns Can Be Mined? • What Technology Are Used? • What Kind of Applications Are Targeted? • Major Issues in Data Mining • A Brief History of Data Mining and Data Mining Society
- 3. • Database-oriented data sets and applications – Relational database, data warehouse, transactional database • Advanced data sets and advanced applications – Data streams and sensor data – Time-series data, temporal data, sequence data (incl. bio-sequences) – Structure data, graphs, social networks and multi-linked data – Object-relational databases – Heterogeneous databases and legacy databases – Spatial data and spatiotemporal data – Multimedia database
- 4. • Information integration and data warehouse construction – Data cleaning, transformation, integration, and multidimensional data model • Data cube technology – Scalable methods for computing (i.e., materializing) multidimensional aggregates – OLAP (online analytical processing) • Multidimensional concept description: Characterization and discrimination – Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet region
- 5. • Classification and label prediction – Construct models (functions) based on some training examples – Describe and distinguish classes or concepts for future prediction • E.g., classify countries based on (climate), or classify cars based on (gas mileage) – Predict some unknown class labels • Typical methods – Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, … • Typical applications: – Credit card fraud detection, direct marketing, classifying stars, diseases, web-pages, …