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Breadcrumb:
Data Mining - (Frequent itemsets|co-occurring items)
Data Mining - (Frequent itemsets|co-occurring items)
> (Statistics|Probability|Machine Learning|Data Mining|Data and Knowledge Discovery|Pattern Recognition|Data Science|Data Analysis)
Table of Contents
1 - Articles Related
2 - Documentation / Reference
1 - Articles Related
Data Mining - Apriori algorithm
2 - Documentation / Reference
SPMF|Frequent Itemset Mining Algorithms
data_mining/frequent_itemset.txt
· Last modified: 2013/03/09 22:42 by
gerardnico
(Statistics|Probability|Machine Learning|Data Mining|Data and Knowledge Discovery|Pattern Recognition|Data Science|Data Analysis)
302 pages
The 1 Percent Rule
(Absolute|True) Zero
(Parameters|Model) (Accuracy|Precision|Fit|Performance) Metrics
Adjusted R^2
Akaike information criterion (AIC)
Algorithms
(Anomaly|outlier) Detection
Apriori algorithm
Association (Rules Function|Model) - Market Basket Analysis
Attribute (Importance|Selection) - Affinity Analysis
Area under the curve (AUC)
Automatic Discovery
Bootstrap aggregating (bagging)
(Base rate fallacy|Bonferroni's principle)
(Baseline|Naive) classification (Zero R)
Bayes’ Theorem (Probability)
Bayesian
Benford's law (frequency distribution of digits)
Best Subset Selection Regression
Bias (Sampling error)
Bias-variance trade-off (between overfitting and underfitting)
Bayesian Information Criterion (BIC)
R (Big R)
Bimodal Distribution
Binary logistic regression
Mathematics - (Combination|Binomial coefficient|n choose k)
(Probability|Statistics) - Binomial Distribution
Data Mining, Book
(Boosting|Gradient Boosting|Boosting trees)
Decision boundary Visualization
(C4.5|J48) algorithm
(Case-control|retrospective) sampling
Causation - Causality (Cause and Effect) Relationship
Cumulative Distribution Function (CDF)
Centering Continous Predictors
Central limit theorem (CLT)
Centroid (center of gravity)
Chance
Customer - Churn Analysis (Customer retention)
(Class|Category|Label) Target
(Classifier|Classification Function)
Clustering (Function|Model)
(Prediction|Recommender System) - Collaborative filtering
Competitions (Kaggle and others)
Statistics - (Confidence|likelihood) (Prediction probabilities|Probability classification)
Confidence Interval
Confounding (factor|variable) - (Confound|Confounder)
Confusion Matrix
Content Analysis and Acquisition
Continuous Variable
Convex
Correlation (Coefficient analysis)
Cosine Similarity (Measure of Angle)
Covariance
Mallow's Cp
Cross Product (of X and Y) (CP|SP)
(Statistics|Data Mining) - (K-Fold) Cross-validation (rotation estimation)
(Periodicity|Periodic phenomena|Cycle)
(Data|Knowledge) Discovery - Statistical Learning
Data Point
Data (Preparation | Wrangling | Munging)
Data Product
Data - Science
Data Scientist
Decision Tree (DT) Algorithm
Decision Stump
Deep Learning (Network)
(Degree|Level) of confidence
Degree of freedom (df)
(dependent|paired sample) t-test
Math - Derivative (Sensitivity to Change, Differentiation)
Design Matrix (X)
Deviance
Deviation Score (for one observation)
Dimensionality (number of variable, parameter) (P)
(Dimension|Feature) (Reduction)
(Data|Text) Mining - Word-sense disambiguation (WSD)
(Discretizing|binning) (bin)
Discriminant analysis
Quadratic discriminant analysis (QDA)
(Discriminative|conditional) models
Distance
(Probability|Sampling) Distribution
Dummy (Coding|Variable) - One-hot-encoding (OHE)
Effects (between predictor variable)
Effect Size
Elastic Net Model
Ensemble Learning (meta set)
Entropy (Information Gain)
Prediction Error (Training versus Test)
(Error|misclassification) Rate - false (positives|negatives)
(Estimation|Approximation)
(Estimator|Point Estimate) - Predicted (Score|Target|Outcome|...)
Data analysis - Explanatory
Exponential Distribution
F-distributions
(F-Statistic|F-test|F-ratio)
Face Recognition
(Factor Variable|Qualitative Predictor)
Factor Analysis
Factorial Anova
Feature Engineering
(Feature|Attribute) Extraction Function
Feature Hashing
(Attribute|Feature) (Selection|Importance)
Fraud Detection
(Frequency|Rate)
(Frequent itemsets|co-occurring items)
Frequentist
Data Model - Fudge factor
Fuzzy Logic (Partial Truth)
Generalized additive model (GAM)
Gaussian processes (modelling probability distributions over functions)
Generalized Boosted Regression Models
Generative Model
Getting Started
Generalized Linear Models (GLM) - Extensions of the Linear Model
(Stochastic) Gradient descent (SGD)
User Group
Grouping
Head
Hierarchical Clustering
Hierarchy
High Dimension (Curse of Dimensionality)
Data Science - History
Homoscedasticity
Hypothesis (Tests|Testing)
ID3 Algorithm
Intrusion detection systems (IDS)
Image classification
Independent t-test
Statistical - Inference
Information Gain
Information Retrieval
(Interaction|Synergy) effect
Intercept - Regression (coefficient|constant) <math>B_0</math>
Model Interpretation
(Interval|Delta) (Measurement)
Java API for data mining (JDM)
K-Means Clustering algorithm
Kernel
K-Nearest Neighbors (KNN) algorithm - Instance based learning
Knots (Cut points)
Kurtosis (Distribution Tail extremity)
Statistical Learning - Lasso
Standard Least Squares Fit (Guassian linear model)
Leptokurtic distribution
(Level|Label)
(Lying|Lie)
(Life cycle|Project|Data Pipeline)
Lift Chart
Statistical Learning - Simple Linear Discriminant Analysis (LDA)
Fisher (Multiple Linear Discriminant Analysis|multi-variant Gaussian)
Linear (Regression|Model)
(Linear spline|Piecewise linear function)
Little r - (Pearson product-moment Correlation coefficient)
Global vs Local
LOcal (Weighted) regrESSion (LOESS|LOWESS)
Log-likelihood function (cross-entropy)
Logistic regression (Classification Algorithm)
(Logit|Logistic) (Function|Transformation)
Loss functions (Incorrect predictions penalty)
Data Science - (Kalman Filtering|Linear quadratic estimation (LQE))
Machine Learning
Main Effect
Probability mass function (PMF)
Maximum
Maximum Entropy Algorithm
Maximum likelihood
Measure
(Scales of measurement|Type of variables)
(Missing Value|Not Available)
Model Size (d)
Model vs Expert
Moderator Variable (Z) - Moderation
Monte Carlo (method|experiment) (stochastic process simulations)
(Average|Mean) Squared (MS) prediction error (MSE)
Multi-variant logistic regression
Multi-class (classification|problem)
(Multiclass Logistic|multinomial) Regression
Multidimensional scaling ( similarity of individual cases in a dataset)
Multiple Linear Regression
Naive Bayes (NB)
(Probabilistic?) Neural Network (PNN)
(No Predictor|Mean|Null) Model
Noise (Unwanted variation)
Non-linear (effect|function|model)
Non-Negative Matrix Factorization (NMF) Algorithm
Multi-response linear regression (Linear Decision trees)
(Normal|Gaussian) Distribution - Bell Curve
Orthogonal Partitioning Clustering (O-Cluster or OC) algorithm
Odds (Ratio)
(One|Simple) Rule - (One Level Decision Tree)
Outliers Cases
(Overfitting|Overtraining|Robust|Generalization) (Underfitting)
Data Science - Over-generalization
(Paretian|Power law) distribution
Pareto ( Principle | Distribution )
Pattern
Principal Component (Analysis|Regression) (PCA)
(Probability) Density Function (PDF)
Mathematics - Permutation (Ordered Combination)
Piecewise polynomials
Partial least squares (PLS)
Predictive Model Markup Language (PMML)
Poisson (Process|distribution)
(Global) Polynomial Regression (Degree)
Population Parameter
Post-hoc test
Power of a test
(Prediction|Guess)
Predictive Model Markup Language (PMML)
(Machine|Statistical) Learning - (Predictor|Feature|Regressor|Characteristic) - (Independent|Explanatory) Variable (X)
Privacy (Anonymization)
Probability
Probit Regression (probability on binary problem)
Problem
Process control (SPC)
Pruning (a decision tree, decision rules)
R-squared (<math>R^2</math>|Coefficient of determination) for Model Accuracy
Random forest
Random Variable
Range
Rare Event
(Fraction|Ratio|Percentage|Share) (Variable|Measurement)
Raw score
Regression
(Regression Coefficient|Weight|Slope) (B)
Assumptions underlying correlation and regression analysis (Never trust summary statistics alone)
(Machine learning|Inverse problems) - Regularization
Reinforcement learning
Sampling - Sampling (With|without) replacement (WR|WOR)
Research
(Residual|Error Term|Prediction error|Deviation) (e|<math>\epsilon</math>)
Resistant
Result Considerations
Ridge regression
Root Mean Square (RMS)
Root mean squared (Error|Deviation) (RMSE|RMSD)
ROC Plot and Area under the curve (AUC)
Rote Classifier
Residual sum of Squares (RSS) = Squared loss ?
(Decision) Rule
Sampling
Sampling Distribution
Sampling Error
Scale
Scoring (Applying)
(Random) Seed
(Shrinkage|Regularization) of Regression Coefficients
Signal (Wanted Variation)
Significance level
(Significance | Significant) Effect
Similarity
Simple Effect
(Univariate|Simple) Logistic regression
(Univariate|Simple|Basic) Linear Regression
Skew (-ed Distribution|Variable)
( Spread | Variability ) of a sample
Stacking
Standard Deviation (SD|s|<math>\sigma</math>|RMS width)
Standard Error (SE)
(Normalize|Standardize)
Statistic
Forward and Backward Stepwise (Selection|Regression)
(Stochastic|random) process
(Supervised|Directed) Learning ("Training") (Problem)
Support Vector Machines (SVM) algorithm
Singular Value Decomposition (SVD)
(Student's) t-test (Mean Comparison)
T-distributions
Tail
(Machine|Statistical) Learning - (Target|Learned|Outcome|Dependent|Response) (Attribute|Variable) (Y|DV)
Test
(Test|Expected|Generalization) Error
Test Set
(Threshold|Cut-off) of binary classification
Titanic Data Set
Training Error
Training (Data|Set)
Nested (Transactional|Historical) Data
Transform
Treatments (Combination of factor level)
True score (Classical test theory)
(True Function|Truth)
(Total) Sum of the square (TSS|SS)
Tuning Parameter
(two class|binary) classification problem
Statistical Learning - Two-fold validation
Data - Uncertainty
Uniform Distribution (platykurtic)
Unsupervised Learning ("Mining")
Resampling through Random Percentage Split
Validity (Valid Measures)
(Variance|Dispersion|Mean Square) (MS)
Variation (Change?)
Probability and Vizualization
Statistics vs (Machine Learning|Data Mining)
Random Walk
(Golf|Weather) Data Set
Z Scale
Z Score (Zero Mean) or Standard Score
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