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Clustering IN DATA MINING CONCEPTS- aLGORITHMNS
- 1. Clustering: Data AttributeTypes in Data Mining Understanding the Impact of Data Types on Clustering Algorithms
- 2. Introduction to Clustering •Clustering is an unsupervised machine learning technique that groups similar data points together. • The type of data attributes plays a crucial role in determining: • The appropriate clustering algorithm • The similarity or distance measure • The performance and interpretability of the resulting clusters
- 3. Numerical Attributes • Definition:Represent quantifiable, continuous measurements. • Examples: Age, temperature, height, income • Clustering Algorithms Used: • K-means: partitions data into k clusters based on minimizing variance. • DBSCAN: groups based on density and distance. • Distance Metrics: • Euclidean Distance • Manhattan Distance • Preprocessing: • Normalization/standardization is important to scale features equally.
- 4. Categorical Attributes • Definition:Non-numeric data that represents categories or labels with no intrinsic order. • Examples: Gender, product type, yes/no survey answers • Clustering Algorithms Used: • K-modes: uses matching dissimilarity instead of distance. • Hierarchical clustering with categorical compatibility measures • Distance Metrics: • Matching dissimilarity (e.g., count of mismatches) • Jaccard index (for binary attributes) • Preprocessing: • Use one-hot encoding if combining with numerical features
- 5. Mixed Data Types •Definition: Datasets that contain both numerical and categorical attributes. • Example: A customer profile with age (numerical) and occupation (categorical) • Clustering Algorithms Used: • K-prototypes: Combines K-means and K-modes to handle both types. • PAM (Partitioning Around Medoids) with Gower distance • Challenge: Combining numerical and categorical features requires specialized similarity measures.
- 6. Impact of DataAttributes • Distance Metrics: • Numerical data: Euclidean, Manhattan • Categorical data: Matching dissimilarity, Jaccard index • Algorithm Selection: • K-means for numerical • K-modes for categorical • K-prototypes or PAM + Gower for mixed • Performance: • Irrelevant or unscaled attributes reduce clustering quality. • Proper feature selection improves both accuracy and interpretability.
- 7. Challenges in Clustering •High Dimensionality: • Distance becomes less meaningful in high dimensions (curse of dimensionality) • Mixed Data: • Difficult to balance impact of numerical vs. categorical features • Interpretability: • Large numbers of features or unclear cluster boundaries reduce usefulness
- 8. Preprocessing Techniques • NumericalAttributes: • Apply scaling (Min-Max or Z-score normalization) • Categorical Attributes: • Encode with one-hot or label encoding (based on algorithm requirements) • Mixed Data Types: • Use Gower distance or clustering algorithms designed for mixed types (e.g., K- prototypes) • Consider dimensionality reduction (PCA, t-SNE) for visualization
- 9. Summary • Data attributetypes are foundational in clustering tasks • Appropriate selection of algorithms, distance metrics, and preprocessing steps is essential • Key Takeaways: • Understand your data’s attribute types • Match the algorithm to the data type • Use proper preprocessing to enhance results
- 10. Similarity and dissimilarity •Similarityand dissimilarity are fundamental concepts in data mining. •They help in: •Clustering •Classification •Recommendation systems •Similarity: How alike two objects are. •Dissimilarity (or distance): How different two objects are.
- 11. Similarity vs. Dissimilarity FeatureSimilarity Dissimilarity Meaning Measures likeness between objects Measures difference between objects Range Usually between 0 and 1 Usually ≥ 0 (0 = same, higher = more different) Ideal Value 1 = identical, 0 = different 0 = identical, larger = different Examples Cosine similarity, Jaccard similarity Euclidean distance, Manhattan distance
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