Six Key Terms Defined (pick any four for the exam)

(a) Occam's Razor

A principle stating that among competing hypotheses that fit the data equally well, the simplest one should be preferred. In data mining/ML, this motivates choosing simpler models (fewer parameters, shallower decision trees) over needlessly complex ones, since simpler models generalize better and are less prone to overfitting.

(b) ROC Curve

Receiver Operating Characteristic curve — plots the True Positive Rate (Sensitivity) against the False Positive Rate (1−Specificity) at various classification thresholds.

TPR
 1 │        ●───●
   │      ●
   │    ●
   │  ●
   │●
 0 └──────────────── FPR
   0              1

The Area Under the Curve (AUC) summarizes overall classifier performance — AUC=1 is a perfect classifier, AUC=0.5 is random guessing (the diagonal line).

(c) Vector Space Model (VSM)

An algebraic model used mainly in text/document mining and information retrieval, where each document (or object) is represented as a vector of feature weights (e.g., TF-IDF term weights) in a high-dimensional space. Similarity between documents is then computed geometrically, typically using cosine similarity between their vectors.

(d) Data Marts

A subset of a data warehouse, scoped to a specific business line, department, or subject area (e.g., a Sales data mart, an HR data mart). Data marts are smaller, faster to query, and easier to maintain than the full enterprise warehouse, letting individual teams get focused analytical access without querying the entire warehouse.

(e) Multilevel Association Rules

Association rules mined at different levels of a concept hierarchy rather than only at the raw item level. For example, instead of just "{Bread} ⇒ {Butter}", multilevel mining can surface rules at higher abstraction levels like "{Bakery Items} ⇒ {Dairy Items}", or at lower levels like "{Whole Wheat Bread} ⇒ {Salted Butter}". This reveals patterns that might be too sparse (low support) at the finest level but become significant when items are generalized.

(f) OLAM (On-Line Analytical Mining)

Integrates OLAP with data mining, allowing mining algorithms to be applied directly on multidimensional data cubes rather than flat/raw data. This lets analysts interactively drill/roll/slice/dice to narrow down interesting regions of the cube, then apply mining functions (classification, clustering, association) at whatever granularity they've navigated to — combining OLAP's exploratory interactivity with mining's automatic pattern discovery.