Data Mining and Analysis
Expert-defined terms from the Undergraduate Certificate in AI for Indirect Tax Management course at Greenwich School of Business and Finance. Free to read, free to share, paired with a globally recognised certification pathway.
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Association Rule Mining #
Association rule mining is a data mining technique used to discover interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using measures of interestingness.
Attribute #
An attribute is a characteristic or feature of an object. In the context of data mining, an attribute is a column in a database table that describes a characteristic of the object being studied.
Attribute Selection #
Attribute selection is the process of selecting a subset of relevant attributes for use in data mining tasks. This can help improve the accuracy and efficiency of data mining algorithms.
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Bayesian Network #
A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies using a directed acyclic graph (DAG). It is used to reason about uncertain knowledge and make probabilistic inferences.
Binning #
Binning is the process of dividing a continuous attribute into a set of intervals, or bins. This is often done to simplify the data and make it more suitable for data mining algorithms.
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Classification #
Classification is a data mining task that involves building a model that can predict the class or category of a given instance based on its attributes.
Clustering #
Clustering is a data mining task that involves grouping similar instances together based on their attributes.
CNN (Convolutional Neural Network) #
A CNN is a type of neural network that is commonly used for image recognition tasks. It is designed to automatically and adaptively learn spatial hierarchies of features from images.
Correlation #
Correlation is a statistical measure that describes the degree to which two variables move in relation to each other.
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Decision Tree #
A decision tree is a type of machine learning model that uses a tree-like structure to make decisions based on the attributes of a given instance.
Dimensionality Reduction #
Dimensionality reduction is the process of reducing the number of attributes or features in a dataset. This can help improve the performance and interpretability of data mining algorithms.
Discriminant Analysis #
Discriminant analysis is a statistical technique used to classify or predict the group membership of instances based on their attributes.
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Ensemble Learning #
Ensemble learning is a machine learning technique that involves combining the predictions of multiple models to improve the accuracy and robustness of the final prediction.
Entropy #
Entropy is a measure of the disorder or randomness of a system. In the context of data mining, entropy is used to evaluate the purity of a set of instances and determine the optimal split in a decision tree.
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Feature Engineering #
Feature engineering is the process of creating new features or attributes from the existing data in a dataset. This can help improve the performance and interpretability of data mining algorithms.
Feature Selection #
Feature selection is the process of selecting a subset of relevant features or attributes for use in data mining tasks. This can help improve the accuracy and efficiency of data mining algorithms.
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Genetic Algorithm #
A genetic algorithm is a optimization technique inspired by the process of natural selection. It is used to find the optimal solution to a problem by iteratively evolving a population of candidate solutions.
Graphical Model #
A graphical model is a statistical model that represents a set of variables and their dependencies using a graph. It is used to reason about uncertain knowledge and make probabilistic inferences.
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Hierarchical Clustering #
Hierarchical clustering is a clustering technique that involves grouping instances into a hierarchical structure of clusters.
Homoscedasticity #
Homoscedasticity is the property of a statistical model where the variance of the errors is constant across all levels of the predictor variables.
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Instance #
An instance is a single observation or data point in a dataset.
Information Gain #
Information gain is a measure of the reduction in entropy that results from splitting a set of instances based on a given attribute. It is used to evaluate the quality of a split in a decision tree.
Instance #
Based Learning: Instance-based learning is a machine learning technique that involves making predictions based on the similarity of the given instance to instances in the training data.
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K #
Means Clustering: K-means clustering is a clustering technique that involves partitioning a set of instances into k clusters based on their attributes.
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Nearest Neighbors (KNN): KNN is a instance-based learning algorithm used for classification and regression tasks. It works by finding the k instances in the training data that are most similar to the given instance and using their labels or values to make a prediction.
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Latent Variable #
A latent variable is a hidden or unobserved variable that is not directly measured but is inferred from other observed variables.
Linear Regression #
Linear regression is a statistical model used to predict a continuous variable based on one or more predictor variables.
Logistic Regression #
Logistic regression is a statistical model used to predict a binary variable based on one or more predictor variables.
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Naive Bayes #
Naive Bayes is a probabilistic machine learning algorithm used for classification tasks. It is based on Bayes' theorem and assumes that the attributes are independent of each other given the class.
Neural Network #
A neural network is a machine learning model inspired by the structure and function of the human brain. It consists of interconnected nodes or neurons that process and transmit information.
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Overfitting #
Overfitting is a common problem in machine learning where a model is excessively complex and fits the training data too closely, resulting in poor generalization performance on new data.
Outlier #
An outlier is a data point that is significantly different from the other data points in the dataset.
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Principal Component Analysis (PCA) #
PCA is a dimensionality reduction technique that involves transforming the original attributes into a new set of uncorrelated attributes called principal components.
Probability Density Function (PDF) #
A PDF is a function that describes the probability distribution of a continuous random variable.
Probability Mass Function (PMF) #
A PMF is a function that describes the probability distribution of a discrete random variable.
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Random Forest #
Random forest is an ensemble learning algorithm that involves building multiple decision trees and combining their predictions to make a final prediction.
Regression #
Regression is a data mining task that involves building a model that can predict a continuous variable based on its attributes.
Resampling #
Resampling is a technique used to estimate the performance of a machine learning algorithm by repeatedly drawing samples from the training data and evaluating the algorithm on those samples.
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SVM (Support Vector Machine) #
SVM is a machine learning algorithm used for classification and regression tasks. It works by finding the hyperplane that maximally separates the classes or predicts the values with the minimum error.
Supervised Learning #
Supervised learning is a machine learning paradigm where the model is trained on labeled data to make predictions on new,