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Computer Science

Data Mining

Association Rule Learning

Anomaly Detection Methods

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Clustering Algorithms

Classification Algorithms

Data Dimensionality Reduction Techniques

Data Mining Project Lifecycle

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Data Preprocessing Techniques

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Ensuring Data Privacy in Data Mining

Ethical Aspects of Data Mining

Frequent Pattern Mining Algorithms

Machine Learning vs Data Mining

Mining Complex Data Types

Multimedia Data Mining

Predictive Analytics Fundamentals

Time Series Analysis in Data Mining

Web Mining Methods

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Spatial Data Mining

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Clustering Algorithms

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Hierarchical Clustering

Builds a tree of clusters by either successively merging or splitting clusters based on a distance measure. Ideal for discovering hierarchical relationships and when the number of clusters is not known.

OPTICS (Ordering Points To Identify the Clustering Structure)

Similar to DBSCAN, but creates an ordered list of points representing a clustering structure. Ideal for data with varying density and when cluster separation is not clear.

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Clusters data based on density estimation and is robust to outliers. It can discover clusters of arbitrary shape and is ideal for spatial data with noise.

K-Means Clustering

A partitioning method that divides data into non-overlapping subsets (clusters) by minimizing the variance within each cluster. Ideal for spherical cluster shapes and large datasets.

Affinity Propagation

Exchanges messages between data points until a set of exemplars (cluster centers) emerges. Ideal for small to medium size datasets and when the number of clusters is not known.

Mean Shift Clustering

A centroid-based algorithm that updates candidates for centroids to be the mean of the points within a given region. Ideal for complex cluster shapes and when the number of clusters is not known.

Spectral Clustering

Uses eigenvalues of a similarity matrix to reduce dimensionality before clustering in fewer dimensions. It's ideal for clustering non-convex clusters or when the graph representation of data is available.

Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

A probabilistic model that assumes data points are generated from a mixture of several Gaussian distributions. Ideal for soft-clustering and when there is a hidden, not observable parameter.

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