Table of Contents

## Which clustering is more efficient?

k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers. This course focuses on k-means because it is an efficient, effective, and simple clustering algorithm. Figure 1: Example of centroid-based clustering.

**How do you measure effectiveness of a cluster?**

Clustering Performance Evaluation Metrics Here clusters are evaluated based on some similarity or dissimilarity measure such as the distance between cluster points. If the clustering algorithm separates dissimilar observations apart and similar observations together, then it has performed well.

**Which cluster method is best?**

Density-based clustering is also a good choice if your data contains noise or your resulted cluster can be of arbitrary shapes. Moreover, these types of algorithms can deal with dataset outliers more efficiently than the other types of algorithms.

### What are different types of clusters?

The various types of clustering are:

- Connectivity-based Clustering (Hierarchical clustering)
- Centroids-based Clustering (Partitioning methods)
- Distribution-based Clustering.
- Density-based Clustering (Model-based methods)
- Fuzzy Clustering.
- Constraint-based (Supervised Clustering)

**What is the fastest clustering algorithm?**

If it is well-separated clusters, then k-means is the fastest. If it is overlapping dataset, then efficiency and effectiveness are both important, thus fuzzy clustering methods are recommended solutions.

**How do you choose a cluster?**

The optimal number of clusters can be defined as follow: Compute clustering algorithm (e.g., k-means clustering) for different values of k. For instance, by varying k from 1 to 10 clusters. For each k, calculate the total within-cluster sum of square (wss).

#### What is the difference between K mean and em?

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

**Which type of clustering is used for big data?**

K-means clustering algorithm K-means clustering is the most commonly used clustering algorithm. It’s a centroid-based algorithm and the simplest unsupervised learning algorithm. This algorithm tries to minimize the variance of data points within a cluster.

**What is cluster technique?**

3.4. Clustering techniques consider data tuples as objects. They partition the objects into groups, or clusters, so that objects within a cluster are “similar” to one another and “dissimilar” to objects in other clusters.

## Which are the two types of clustering?

2. Types of Clustering

- Hard Clustering: In hard clustering, each data point either belongs to a cluster completely or not.
- Soft Clustering: In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned.

**Which is not type of clustering?**

option3: K – nearest neighbor method is used for regression & classification but not for clustering. option4: Agglomerative method uses the bottom-up approach in which each cluster can further divide into sub-clusters i.e. it builds a hierarchy of clusters.