Comprehensiuhyhy8ve Guide to Clustering.pdf

ppt

1. RAMKRISHNA MAHATO GOVRNMENT ENGINEERING COLLEGE
NAME : DEBASISH KATARI
ROLL NO : 35000122035
SUBJECT : PATTERN RECOGNITION
SUBJECT CODE: PEC-IT602D
SEMESTER : 6
TH

DEPARTMENT : COMPUTER SCIENCE & ENGINEERING

TOPIC : CRITERION FUNCTIONS FOR CLUSTERING

3. TYPES OF CLUSTERING ALGORITHMS
•Partition-based Algorithms: K-Means and similar methods cluster data by partitioning
into distinct groups based on centroid proximity.
•Hierarchical Clustering: Utilizes a tree-like structure, enabling both agglomerative and
divisive approaches for data grouping hierarchies.
•Density-based Methods: Algorithms like DBSCAN identify clusters based on dense
regions, effectively handling noise and varied densities.

4. CREATION FUNCTIONS IN CLUSTERING
•Creation Functions Defined: Creation functions initialize cluster centroids, critically
impacting the optimization and convergence of clustering algorithms.
•Role in Clustering: They enhance starting point selection, facilitating faster convergence
to optimal clusters and improved end results.
•Impact on Performance: Effective initialization significantly reduces computation time
and enhances overall clustering performance and interpretation accuracy.

5. K-MEANS INITIALIZATION FUNCTIONS
•Random Initialization: This method selects
centroids randomly, which may lead to
inconsistent clustering results and slower
convergence.
•K-Means++ Initialization: It strategically
selects initial centroids, improving
convergence speed and leading to more
stable clustering outcomes.
•Influence on Clustering Results: Choosing
the right initialization method directly impacts
cluster quality, reducing variance and
enhancing model robustness. Generated on AIDOCMAKER.COM

6. HIERARCHICAL CLUSTERING INITIALIZATION
•Single-Linkage Method: This method merges clusters based on the shortest distance
between points from different clusters.
•Dendrogram Construction: A dendrogram visually represents hierarchical relationships,
illustrating merging steps and cluster similarity levels.
•Complete-Linkage Method: Clusters are merged by maximizing inter-cluster distances,
promoting compact and well-separated final clusters.

7. DBSCAN AND DENSITY -BASED METHODS
•Introduction to DBSCAN: DBSCAN, a density-based clustering algorithm, groups points in
dense areas and separates noise effectively.
•Key Parameters: MinPts & Epsilon: MinPts defines minimum cluster size; Epsilon sets
neighborhood radius for point inclusion, guiding cluster formation.
•Handling Noise Points: DBSCAN identifies outliers by marking points not within any
cluster as noise, improving robustness against anomalies.

8. GAUSSIAN MIXTURE MODELS (GMM) AND EM
ALGORITHM
•Gaussian Mixture Models Overview:
GMMs represent data as a mixture of multiple
Gaussian distributions, allowing for soft
clustering probability assessments.
•Expectation-Maximization Algorithm: The
EM algorithm iteratively optimizes
parameters by alternating between
estimating latent data and maximizing
likelihood.
•Initialization Effects on GMM: Choice of
initialization method substantially impacts
GMM convergence speed and accuracy of
resulting clusters' representation.
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9. CLUSTERING PERFORMANCE METRICS
•Silhouette Score: Measures how similar an object is to its own cluster compared to others,
indicating separation quality.
•Davies-Bouldin Index: Quantifies the average ratio of within-cluster distances to
between-cluster distances, assessing cluster compactness and separation.
•Within-cluster Sum of Squares: Calculates total variance within each cluster, providing
insights into cluster cohesion and compactness for performance evaluation.

10. CHALLENGES IN CLUSTERING
•Outlier Management: Effectively managing outliers is crucial as they can skew cluster
centroids and misrepresent data relationships.
•Optimal Cluster Count: Determining the ideal number of clusters requires methods like
the elbow method or silhouette analysis for validation.
•Computational Challenges: Large datasets pose computational complexity issues,
necessitating efficient algorithms to maintain reasonable processing times.

11. CONCLUSION AND FUTURE TRENDS
•Advances in Deep Clustering: Recent
research integrates deep learning with
clustering, enhancing feature extraction and
representation for better accuracy.
•AI-Driven Methods: Innovation in AI-driven
clustering methods utilizes neural networks to
dynamically adapt and improve cluster
formation processes.
•Future Research Directions: Potential
research examines unsupervised learning
improvements and integration with
reinforcement learning for optimized
clustering techniques.
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12. THANK YOU