Data mining algorithms are essential tools in Advanced Quantitative Methods, Business Analytics, and Business Intelligence. They help uncover patterns and insights from complex datasets, enabling better decision-making and predictive analysis across various business applications.
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Decision Trees
- A tree-like model used for classification and regression tasks.
- Splits data into subsets based on feature values, creating branches for decisions.
- Easy to interpret and visualize, making it user-friendly.
- Prone to overfitting, especially with complex datasets.
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Random Forests
- An ensemble method that combines multiple decision trees to improve accuracy.
- Reduces overfitting by averaging predictions from various trees.
- Handles large datasets with higher dimensionality effectively.
- Provides feature importance scores, aiding in variable selection.
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Support Vector Machines (SVM)
- A supervised learning model used for classification and regression.
- Finds the optimal hyperplane that maximizes the margin between classes.
- Effective in high-dimensional spaces and with non-linear boundaries using kernel functions.
- Sensitive to the choice of kernel and regularization parameters.
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K-Nearest Neighbors (KNN)
- A non-parametric, instance-based learning algorithm for classification and regression.
- Classifies data points based on the majority class of their nearest neighbors.
- Simple to implement but can be computationally expensive with large datasets.
- Sensitive to the choice of distance metric and the value of K.
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Naive Bayes
- A probabilistic classifier based on Bayes' theorem with an assumption of feature independence.
- Works well with high-dimensional data and is particularly effective for text classification.
- Fast and efficient, requiring a small amount of training data.
- Assumes that all features contribute equally to the outcome, which may not always be true.
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K-Means Clustering
- An unsupervised learning algorithm that partitions data into K distinct clusters.
- Iteratively assigns data points to the nearest cluster centroid and updates centroids.
- Sensitive to the initial placement of centroids and the choice of K.
- Works best with spherical clusters and requires numerical data.
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Hierarchical Clustering
- Builds a tree-like structure (dendrogram) to represent data clusters.
- Can be agglomerative (bottom-up) or divisive (top-down) in approach.
- Does not require a predefined number of clusters, allowing for flexible analysis.
- Computationally intensive for large datasets due to pairwise distance calculations.
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Principal Component Analysis (PCA)
- A dimensionality reduction technique that transforms data into a lower-dimensional space.
- Identifies the directions (principal components) that maximize variance in the data.
- Helps in visualizing high-dimensional data and reducing noise.
- Assumes linear relationships among features and may not capture complex patterns.
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Association Rule Mining (e.g., Apriori algorithm)
- A method for discovering interesting relationships between variables in large datasets.
- Generates rules based on support, confidence, and lift metrics.
- Commonly used in market basket analysis to identify product purchase patterns.
- Requires careful tuning of parameters to avoid generating too many trivial rules.
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Neural Networks
- A set of algorithms modeled after the human brain, used for complex pattern recognition.
- Composed of layers of interconnected nodes (neurons) that process input data.
- Capable of learning non-linear relationships and handling large datasets.
- Requires significant computational resources and careful tuning of hyperparameters.
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Logistic Regression
- A statistical method for binary classification that models the probability of an outcome.
- Uses the logistic function to constrain predictions between 0 and 1.
- Interpretable coefficients indicate the effect of predictors on the outcome.
- Assumes a linear relationship between the log-odds of the outcome and predictors.
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Linear Regression
- A method for modeling the relationship between a dependent variable and one or more independent variables.
- Assumes a linear relationship and minimizes the sum of squared errors.
- Provides coefficients that indicate the strength and direction of relationships.
- Sensitive to outliers and assumes homoscedasticity of residuals.
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Gradient Boosting Machines (e.g., XGBoost)
- An ensemble technique that builds models sequentially, correcting errors of previous models.
- Combines weak learners (typically decision trees) to create a strong predictive model.
- Highly efficient and scalable, often used in competitive machine learning.
- Requires careful tuning of parameters to avoid overfitting.
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DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- A clustering algorithm that groups together points that are closely packed while marking outliers.
- Does not require a predefined number of clusters and can find arbitrarily shaped clusters.
- Sensitive to the choice of parameters (epsilon and minimum points).
- Effective for datasets with noise and varying densities.
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Time Series Analysis
- A statistical technique for analyzing time-ordered data points to identify trends, cycles, and seasonal variations.
- Involves methods like ARIMA, seasonal decomposition, and exponential smoothing.
- Useful for forecasting future values based on historical data.
- Requires careful consideration of temporal dependencies and stationarity.