# What is K-means algorithm explain with example?

## What is K-means algorithm explain with example?

K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. It assumes that the number of clusters are already known. It is also called flat clustering algorithm. The number of clusters identified from data by algorithm is represented by ‘K’ in K-means.

### Which algorithm is used in K-means?

The K-means clustering algorithm computes centroids and repeats until the optimal centroid is found. It is presumptively known how many clusters there are. It is also known as the flat clustering algorithm. The number of clusters found from data by the method is denoted by the letter ‘K’ in K-means.

Why do we use K-means algorithm Mcq?

Explanation: K-means clustering produces the final estimate of cluster centroids. 2. Point out the correct statement. Explanation: Some elements may be close to one another according to one distance and farther away according to another.

What is K-means algorithm in data mining?

Kmeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group.

## Which of the following are examples of clustering?

Some of the most popular applications of clustering are:

• Recommendation engines.
• Market segmentation.
• Social network analysis.
• Search result grouping.
• Medical imaging.
• Image segmentation.
• Anomaly detection.

### Which is a common application of cluster analysis?

Clustering analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing. Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns.

In which of the following cases will k-means clustering?

In which of the following cases will K-Means clustering fail to give good results? K-Means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different and the data points follow non-convex shapes.

Which is needed by k-means clustering?

Which of the following function is used for k-means clustering? Explanation: K-means requires a number of clusters.

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