PyQdeck LogoPyQdeckSubjectsSyllabusBlog
Back to the 2022 paper

Module 2: Data Mining Fundamentals and Association Rule Mining

20222m

The 0 (zero) correlation value between two data object indicates

(i) no linear relationship between two objects
(ii) non-linear relationship between two objects
(iii) linear relationship between two objects
(iv) Both (i) and (ii)

Search GoogleAsk AI
Worked SolutionAI Assisted

Answer: (i) no linear relationship between two objects

The Pearson correlation coefficient measures the strength of a linear relationship between two variables/objects, ranging from -1 to +1:

-1              0              +1
Strong        No linear      Strong
negative      relationship   positive
linear                       linear

A correlation of 0 means there is no linear relationship — but importantly, this does not rule out a strong non-linear relationship (e.g., y = x², which can have a correlation near 0 despite being a perfect functional dependency). So option (i) is correct, and (ii) is a common misconception — correlation says nothing definitive about non-linear relationships.

Similar questions

Database Management SystemAn object in databases is equal to ____ + relationships. (i) data (ii) attribute (iii) entity (iv) constraint20192mData MiningJaccard coefficient is frequently used to handle objects consisting of (i) symmetric binary attribute (ii) asymmetric binary attribute (iii) Both (i) and (ii) (iv) None of the above20222mArtificial intelligenceWhat are not represented by using propositional logic? (i) Objects (ii) Relations (iii) Both objects and relations (iv) None of the above20202mSoftware Engineering______ is a measure of the degree of interdependence between modules. (i) Cohesion (ii) Coupling (iii) None of the above (iv) All of the above20202m
PreviousJaccard coefficient is frequently used to handle objects consisting of (i) symmetric binary attribute (ii) asymmetric binary attribute (iii) Both (i) and (ii) (iv) None of the aboveNextThe decision boundaries produced in nearest-neighbor classifiers are (i) rectilinear (ii) oblique (iii) arbitrary (iv) None of the above
AI LearningWeb Platform

Coursify — Personal AI Course Generator

Struggling with a difficult subject or concept? Turn any engineering topic into a structured course with interactive lessons, quizzes, and research-backed explanations.

Try Coursify Free