Exploring Clustering Methods

What is clustering in data analysis?

• Clustering is a method of grouping similar data points together based on their characteristics.

• Clustering is a technique for sorting data into chronological order.

• Clustering is a method for identifying outliers in a dataset.

• Clustering involves analyzing data to find the average value.

Name two major categories of clustering methods.

• Hierarchical clustering, Partitioning clustering

• Model-based clustering

• K-means clustering

• Density-based clustering

What is the K-Means algorithm primarily used for?

• Sorting data into a single list.

• Clustering data into distinct groups.

• Predicting future data points.

• Visualizing data trends over time.

Describe the PAM algorithm and its purpose.

• The PAM algorithm is used for clustering data by selecting representative medoids and minimizing distances within clusters.

• The PAM algorithm is designed for image processing by enhancing the quality of images.

• The PAM algorithm is used for sorting data by arranging it in ascending order.

• The PAM algorithm is a machine learning technique for predicting future values based on past data.

What are the two main types of hierarchical clustering?

• Agglomerative and Divisive

• Single-linkage and Complete-linkage

• K-means and DBSCAN

• Random and Fixed-point

Explain the basic agglomerative hierarchical clustering algorithm.

• Agglomerative clustering is a method that only works with categorical data.

• Agglomerative hierarchical clustering is a clustering method that starts with individual data points as clusters and merges them based on distance until a single cluster is formed or a desired number of clusters is achieved.

• Agglomerative hierarchical clustering starts with a single cluster and splits it into individual data points.

• The algorithm uses a fixed number of clusters from the beginning and does not merge them.

What are some key issues faced in hierarchical clustering?

• Hierarchical clustering is immune to noise and outliers.

• Key issues in hierarchical clustering include sensitivity to noise and outliers, difficulty in determining the optimal number of clusters, computational inefficiency for large datasets, and dependency on linkage criteria.

• It requires a predefined number of clusters before analysis.

• Hierarchical clustering is always the fastest method available.

List one strength and one weakness of hierarchical clustering.

• Strength: Provides exact cluster assignments; Weakness: Limited to spherical clusters.

• Strength: Visualizes data relationships; Weakness: Computationally expensive for large datasets.

• Strength: Works well with small datasets; Weakness: Cannot handle noise in data.

• Strength: Easy to implement; Weakness: Requires labeled data.

How can outlier detection be related to clustering?

• Clustering algorithms are designed to find outliers in data sets.

• Outlier detection is used to enhance the quality of clusters.

• Outlier detection identifies points that do not fit well into any cluster formed by clustering algorithms.

• Outlier detection and clustering are completely unrelated processes.

What is the difference between partitioning and hierarchical methods?

• Partitioning methods are only applicable to large datasets, while hierarchical methods work for small datasets.

• Partitioning methods use a single cluster, while hierarchical methods use multiple clusters.

• Hierarchical methods create random clusters, while partitioning methods create a fixed number of clusters.

• Partitioning methods create distinct clusters, while hierarchical methods create a tree of clusters.