Paragrah$-1$

Cluster analysis or clustering is the task of grouping a set of objects in such a way that

objects in the same group $($called a cluster$)$ are more similar $($in some sense$)$ to each other

than to those in other groups $($clusters$).$ It is a main task of exploratory data analysis,

and a common technique for statistical data analysis, used in many fields, including pattern

recognition, image analysis, information retrieval, bioinformatics, data compression,

computer graphics and machine learning. It is typically done based on similarity and

differences. Other aspects of continuity are also considered here.

Paragrah$-2$

The principles of grouping $($or Gestalt laws of grouping$)$ are a set of principles in

psychology, first proposed by Gestalt psychologists to account for the observation that

humans naturally perceive objects as organized patterns and objects, a principle known as

Pr$$gnanz$.$ Gestalt psychologists argued that these principles exist because the mind has an

innate disposition to perceive patterns in the stimulus based on certain rules. These

principles are organized into five categories: Proximity, Similarity, Continuity, Closure,

and Connectedness.

$8.$ Apply multi$-$document summarization on $$paragraph$-1$ and $$paragraph$-2.$ Can you use TFIDF in this case? How? Can we have extractive summaries for each of them and then combining them for final outcome? How?

$9.$ What is meant by context pointers? Give an algorithm based on BoW to determine context pointers. How can you derive context pointers $$ in case of $$paragraph$-1$ and $$paragraph$-2.$ Use extractive summarization word$-$based and phrase$-$based for these two paragraphs to derive context pointer