Statistical tools for mining, often referred to as data mining or statistical data analysis, involve a variety of techniques and software designed to extract meaningful patterns, trends, and insights from large datasets. Here are some commonly used statistical tools and techniques in mining:
1. Descriptive Statistics
– Mean, Median, Mode: Measures of central tendency.
– Variance, Standard Deviation: Measures of dispersion.
– Frequency Distribution: Summarizes the occurrence of data points.
2. Inferential Statistics
– Hypothesis Testing: Determines if there is enough evidence to support a specific hypothesis.
– Confidence Intervals: Estimates the range within which a population parameter lies.
– ANOVA (Analysis of Variance): Compares means across multiple groups.
3. Regression Analysis
– Linear Regression: Models the relationship between a dependent variable and one or more independent variables.
– Logist
Regression: Used for binary classification problems.
4. Classification and Clustering
– K-Means Clustering: Partitions data into k distinct clusters based on similarity.
– Hierarchical Clustering: Builds a hierarchy of clusters.
– Decision Trees: A flowchart-like structure for decision-making.
– Random Forests: An ensemble method using multiple decision trees.
5. Dimensionality Reduction
– Principal Component Analysis (PCA): Reduces the number of variables while preserving variance.
– t-SNE (t-Distributed Stochastic Neighbor Embedding): Visualizes high-dimensional data.
6. Association Rule Learning
– Apriori Algorithm: Identifies frequent itemsets and association rules in transactional databases.
– FP-Growth Algorithm: Efficiently mines frequent itemsets without candidate generation.
7. Time Series Analysis
– ARIMA (AutoRegressive Integrated Moving Average): Models time series data for forecasting.
– Exponential Smoothing: A technique for smoothing time series data.
8. Machine Learning Algorithms
– Support Vector Machines (SVM): Classifies data by finding the optimal hyperplane.
– Neural Networks: Models complex patterns using layers of neurons.
9. Software Tools
– R: A programming language and environment for statistical computing and graphics.
– Python (with libraries like Pandas, NumPy, Scikit-learn)