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Computer Science

Data Mining

Association Rule Learning

Anomaly Detection Methods

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Clustering Algorithms

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Data Mining Project Lifecycle

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Data Mining for Fraud Detection

Data Mining in Finance

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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

Social Media Data Mining

Spatial Data Mining

Text Mining Techniques

Spatial Data Mining

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Getis-Ord Gi*

Getis-Ord Gi* is a statistical method to identify 'hot spots' and 'cold spots' within spatial data. It helps in determining areas with high or low values of a particular phenomenon, and is used in crime analysis, public health, and resource management.

Geostatistics

Geostatistics is a branch of statistics focusing on spatial datasets that involves data analysis and modeling for phenomena that vary over space. It's used in mining, hydrology, environmental contamination, and agriculture for spatial data prediction and assessment.

Spatial Autocorrelation

Spatial Autocorrelation is the concept that spatially close objects are likely to share similar values, implying that the presence of a particular attribute is correlated to its location. It's crucial in environmental modeling, epidemiology, and urban planning for identifying patterns and relationships.

Spatial Decision Tree Learning

Spatial Decision Tree Learning extends traditional decision tree algorithms to incorporate spatial data and relationships. Applications include land cover classification, urban growth prediction, and analyzing spatial dynamics in ecological datasets.

Moran's I

Moran's I is a measure of spatial autocorrelation which describes the degree to which similar values cluster together in space. It is often used in public health, ecology, and regional science to detect hot-spots and spatial patterns.

Kriging

Kriging is a geostatistical interpolation technique which makes use of spatial autocorrelation and variograms to make predictions about unknown data points based on the known sampled data points. Applications include prediction of resources, environmental science, and geography.

DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) groups together points that are closely packed together, and marks as outliers points that lie alone in low-density regions. This algorithm is widely used in anomaly detection, spatial data analysis, and computer vision.

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