Data Mining algorithms PPT with Overview explanation.
- 1. Seminar Title: Data Mining Algorithms • Group Members: Aliasgar Bootwala Meet Agrawal Faizan Shaikh Roshni Shaikh Institute: SNJB KBJ College of Engineering Chandwad, Nashik Department: Artificial Intelligence and Data Science Year: T.E
- 2. What is Data Mining? • Data mining is a way of obtaining patterns and relationships from data that can be used to make predictions or decisions through data analysis. Enterprises use data mining techniques and tools to predict the future trends of the market and make more business decisions. • No great about of data analysis would pass without the mention of the mechanic that analyzes, cleanses, and models raw data to one that is more significant—data mining. Data mining is a subset of data science and statistics (as well as some fields within it) focused on exploratory data analysis through unsupervised learning. • It also requires effective collection, warehousing, and processing of the data. Data mining can be used to describe a target set of data, predict outcomes, detect fraud or security issues, learn more about a user base, or even detect bottlenecks and dependencies. It can also be performed automatically or semiautomatically.
- 3. Importance of data mining in modern technology 1. Enhancing Decision-making. 2. Predictive Analytics. 3. Customer insights & Personalization. 4. Fraud detection. 5. Advance Health Care. 6. Operational Efficiency improvements. 7. AI and ML Innovation. 8. Social Media & Sentiment Analysis. 9. Intelligent cities &IoT. 10. Educational understanding &Personalization.
- 4. Supervised Learning Classification Logistic Regression Decision Trees Support Vector Machines (SVM) Naïve Bayes k-Nearest Neighbors (kNN) Regression Linear Regression Polynomial Regression
- 5. Supervised Learning • Supervised learning is a type of machine learning where the model is fit on labeled data, meaning each training example includes input-output pairs. • How it Works: The model learns to map inputs to the correct outputs, aiming to predict the output for new, unseen data. • Main Applications: Classification: Determining categories, • Regression: Predicting continuous values. • Examples: Detecting Spam, Translation of language, Sales Forecasting.
- 6. Classification Algorithms 1) Logistic 2) Regression 3) Decision Trees 4) SVM 5) Naive Bayes 6) KNN
- 7. • Logistic Regression: It predicts the probability of categorical outcomes, especially binary like spam/not spam. • Decision Trees: A kind of flowchart where data splits into branches to reach a decision, thereby making it interpretable. • Support Vector Machine (SVM): It finds the best hyperplane that could separately classify different classes for high- dimensional data. • Naive Bayes Bayes' theorem, assuming feature independence, widely used for text classification. • k-Nearest Neighbors (kNN): Classifies the data using the 'k' nearest data points; Works well in low-dimensional spaces.
- 8. Regression Algorithms 1) Linear Regression 2) Polynomial Regression 3) Multivariate Regression Intelligence
- 9. • Linear Regression: Fits a linear relationship between input and output, useful for predicting trends and prices. • Polynomial Regression Extends linear regression to non-linear relationships through the use of polynomial terms. • Multivariate Regression: Predicts a target variable given multiple input features, suitable for more complex datasets. • Applications: Stock price prediction, weather forecasting, and house pricing.
- 10. Unsupervised Learning Clustering k-Means Clustering Hierarchical Clustering DBSCAN Gaussian Mixture Models (GMM) Dimensionality Reduction Principal Component Analysis (PCA) t-SNE Autoencoders Independent Component Analysis (ICA) Machine Learning Techniques
- 11. Unsupervised Learning • Unsupervised learning is a type of machine learning where the model works with unlabeled data, discovering hidden patterns or grouping data without predefined labels. • How it Works: The model explores the structure of the data in order to find clusters or reduce complexity, often for exploratory data analysis. • Major Uses: -Clustering (grouping similar data) -Dimensionality Reduction (simplifying data features). • Examples: Customer segmentation, anomaly detection, and image compression.
- 12. Clustering • The clustering divides data into groups, known as clusters where members of each cluster are more similar to each other than to those in other clusters. • k-Means Clustering: Partitions data into ‘k’ clusters based on centroid similarity, simple and efficient for large datasets. • Hierarchical Clustering: Builds a hierarchy of clusters using either an agglomerative (bottom-up) or divisive (top-down) approach, ideal for visual insights. • DBSCAN (Density-Based Spatial Clustering of Applications with Noise): It groups points depending on the density, hence capturing clusters with irregular shapes and separating outliers. • Gaussian Mixture Model (GMM): Assumes data is generated from multiple Gaussian distributions and assigns probabilities to each data point, useful for more complex clusters.
- 13. Dimensionality Reduction 1) PCA 2) T-SNE 3) Autoencoders 4) ICA
- 14. • Principal Component Analysis (PCA): It transforms features into principal components that capture maximum variance. This is widely used for simplifying high-dimensional data. • t-SNE t-Distributed Stochastic Neighbor Embedding It projects high-dimensional data to 2 or 3 dimensions for visualization preserving the local structure. • Autoencoders: Neural networks that learn a compressed representation of data, commonly used for feature extraction and data denoising. • Independent Component Analysis (ICA): Decompose data into statistically independent components, as applied in the decomposition of mixed signals, e.g., audio sources. Dimensionality Reduction
- 15. Deep Learning • A subset of machine learning using neural networks with multiple layers to extract features from data hierarchically. • Inspired by the structure of the human brain, allowing models to handle complex data patterns.
- 16. Neural Networks Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Long Short-Term Memory Networks (LSTM)
- 17. Neural Networks Types of Neural Networks: • Artificial Neural Networks (ANN): Simple network structure with an input, hidden, and output layer; useful for general applications such as image and text classification. • Convolutional Neural Network (CNN): Designed with specialization towards image and spatial data, utilizing a convolutional layer to detect local patterns. • Recurrent Neural Networks (RNN): Designed for sequential data (e.g. time series, language), where past information affects the present. • Long Short-Term Memory (LSTM): An advanced RNN variant that handles long-term dependencies, ideal for complex sequences like language modeling and speech recognition.
- 18. Neural Networks Applications • Applications and Use Cases: • ANNs: Financial forecasting, spam detection, and recommendation systems. • CNNs: Image and video recognition, object detection, and medical image analysis. • RNNs: Text generation, language translation, and time-series prediction. • LSTMs: Speech to text, music compositing, and predictive text input. • Benefits of Deep Learning: • Automated Feature Extraction: Reduces the need for manual feature engineering. • Scalability: Scale very easily up to large datasets and more complex operations. High Accuracy: Tackles unstructured data better than traditional methods.
- 19. Deep Reinforcement Learning Q-Learning Policy Gradients Deep Q-Networks (DQN)Deep Q- Networks (DQN)
- 20. Deep Reinforcement Learning • Combines deep learning with reinforcement learning where models learn by interacting with environments to maximize rewards. • Key Tools: • Q-Learning: Uses a Q-table or neural network to store action-value pairs for decision-making. • Policy Gradients: learns policies directly by adjusting the probability of actions for maximum reward, especially for complex action spaces. • Deep Q-Networks (DQN): Combining Q-learning and deep neural networks to approximate the Q- values, enabling it to play even video games like Atari. • Some applications of Deep Reinforcement Learning: • Robotics (motion control), game AI, autonomous driving, and optimizing financial portfolios.
- 21. Applications of Data Mining • Marketing Applications: Customer Segmentation: Identifies customer groups based on buying behavior, demographics, and preferences, allowing targeted marketing strategies. Market Basket Analysis: Analyzes purchasing patterns to find products that are often bought together (e.g., Amazon recommendations). Customer Churn Prediction: Helps identify customers likely to leave, enabling proactive retention strategies. • Finance Applications: Credit Scoring and Risk Management: Evaluates customer creditworthiness by analyzing financial history and behaviors to reduce loan default risks. Fraud Detection: Uses patterns and anomaly detection to identify suspicious transactions in real-time, reducing financial fraud. Stock Market Prediction: Employs historical market data to forecast stock trends, helping investors make informed decisions.
- 22. Applications of Data Mining • Healthcare Applications: Disease Prediction and Diagnosis: The dataset is used to predict diseases for patients for early diagnosis (diabetes, cancer, etc.). Health Care Resource Management-Optimize the resource allocation involving hospital beds, medical equipment, and scheduling of staff. Personalized Medicine: It analyzes gene data to create a tailored treatment program based on each patient's specificity. • Retail Applications: Inventory Management: Analyzes sales data to predict demand for products, aiding in stock optimization and reducing wastage. Customer Relationship Management (CRM): It uses the purchasing behavior to enrich customer satisfaction and tailor offers. Price Optimization: Studies the trend of sales to adjust the pricing strategy according to demand, competitiveness, and seasonality.
- 23. Future Trends • AI Incorporation: An increased reliance on AI-rich algorithms that can mine data better autonomously. It enables intelligent data processing to self-learn and adapt, hence improving predictive capabilities. • Big Data and Real-Time Analytics: Big data integration allows for large, complex datasets from IoT devices, social media, and transaction logs to be processed. Real-Time Analysis allows for faster decisions, which is particularly important in areas such as finance (e.g., fraud detection) and retail (e.g., dynamic pricing). • Automation and AutoML: Auto ML (Automated Machine Learning) simplifies the data mining process by automating model selection, tuning, and deployment. No- code / low-code platforms emphasize on increased accessibility towards data mining and reducing time to insight for non-experts.
- 24. Conclusion • Data mining is the process of extracting patterns or unknown accesses from large quantities of data, transforming it into high-value predictions and actionable decisions for all industries. With sophisticated algorithms such as supervised, unsupervised, and semi-supervised learning, data mining allows companies to deep-dive into customer insights, predict trends, detect fraud, and optimize operations. Clustering, classification, and dimensionality reduction will empower businesses in experiences, make them decide fast, and smoothen decision-making processes. Its applications are of paramount importance in healthcare, finance, IoT, and smart cities, contributing both to personalized experiences and operational efficiency. • Integration with AI, automation, and big data technologies will further amply the role of data mining in the future. New entrants like automated machine learning (AutoML) or real-time analytics make accessibility and speed more affordable, while innovations in algorithms based on AI power promise even more precise and intelligent insights from the data. This should keep data mining at the foundation of technological advancement for the next decades, allowing for smarter, more responsive systems that can, in real time, continuously adapt and learn.