Crafting Data Mining Problem Statements & Data-Mining Workflow

Vocabulary flashcards covering central concepts, methods and ethical issues from the lecture on crafting data-mining problem statements and the seven-step analytical workflow.

Practical Motivation and Problem Identification

Stage where one verifies that the challenge is data-related and solvable with available or collectable data (e.g., reducing customer churn).

Data Collection

Process of gathering the relevant, representative, valid and reliable data required to address the formulated problem.

Relevance (in data collection)

The alignment of the collected data’s purpose, scope and specificity with the analytical question being asked.

Representativeness

Extent to which a sample reflects the diversity of the entire population, helping to avoid selection bias.

Validity and Reliability

Qualities that ensure data measures what it claims to measure (validity) and does so consistently (reliability).

Simple Random Sample

Sampling technique where every member of the population has an equal and independent chance of selection.

Systematic Sample

Sampling method that selects every k-th item from an ordered list to create the sample.

Stratified Sample

Sampling approach that divides the population into subgroups (strata) and samples from each to maintain proportional representation.

Cluster Sample

Sampling technique that randomly selects entire groups or clusters, then studies all or a subset of elements within those clusters.

Problem Formulation (Data Mining)

Crafting a clear, specific, answerable analytical question that guides the mining process (e.g., “Can usage patterns predict churn?”).

Data Preparation

Cleaning, integrating, structuring and formatting raw data so it becomes suitable for mining and modeling tasks.

Data from Different Sources

Integrated datasets (e.g., demographics, usage, feedback) that give a comprehensive view but may require complex merging.

Data in a Grid Format

Tabular (rows-columns) organization of data that simplifies analysis but may limit capture of complex relationships.

Exploratory Data Analysis (EDA)

Initial analytical step aimed at discovering patterns, anomalies and basic statistics such as mean, median, variance, distribution.

Mean

Arithmetic average of a numerical data set.

Median

Middle value of an ordered data set, splitting it into two equal halves.

Variance

Statistical metric expressing the degree to which data points spread out from the mean.

Distribution

Overall pattern of values, indicating the shape, center and spread of the data points.

Pattern Recognition

Process of detecting meaningful trends, correlations or structures within data.

Analytical Visualization

Use of charts (e.g., scatter plots, bar charts, heat maps) to display statistical characteristics and support pattern recognition and decisions.

Descriptive Analytics

Techniques that summarize and describe historical data to reveal current patterns and trends.

Inferential Analytics

Statistical techniques that draw conclusions about a larger population based on a sample, often via hypothesis testing or regression.

Statistical Inference

Discipline of drawing reliable, uncertainty-aware conclusions from data analyses.

Generalization (of a model)

Ability of a predictive model to perform accurately on new, unseen data rather than just the training set.

Cross-Validation

Model-evaluation strategy that partitions data into training and testing folds to estimate out-of-sample performance.

Confidence Interval

Range of values that, with a specified probability, contains the true parameter or prediction of interest.

Actionable Intelligence

Insights derived from analysis that directly inform strategic, real-world decisions (e.g., targeting retention campaigns).

Ethical Considerations in Data Mining

Practices ensuring privacy, fairness and compliance with regulations during data handling and analysis.

GDPR (General Data Protection Regulation)

EU legislation governing how personal data must be collected, processed and protected.

Customer Churn Prediction

Data-mining application that identifies customers likely to leave, enabling proactive retention strategies.