Clustering Challenges and Heuristic Solutions

It suffers from computing power constraints resulting in exponential complexity. (correct)

It can only evaluate single partitioning schemes.

It involves testing only a limited number of partitions.

It requires a predefined number of clusters, k, upfront.

The non-existence of clustering algorithms in the literature.

Defining a universal quality function q for clustering. (correct)

Locating the optimal k for the partitioning.

The uniform distribution of data points across clusters.

Develop a universal clustering function applicable to all datasets.

Implement heuristic solutions for efficient search and modeling. (correct)

Use deterministic clustering methods with fixed parameters.

Rely on exhaustive search methods to explore all partitioning schemes.

<p>$O(k^n)$</p> Signup and view all the answers

<p>Clustering presents diverse challenges requiring multiple approaches.</p> Signup and view all the answers