Table of Contents

## How do you do K-means clustering in Python?

Step-1: Select the value of K, to decide the number of clusters to be formed. Step-2: Select random K points which will act as centroids. Step-3: Assign each data point, based on their distance from the randomly selected points (Centroid), to the nearest/closest centroid which will form the predefined clusters.

**What is example of K-means clustering?**

The basic step of k-means clustering is simple. In the beginning we determine number of cluster K and we assume the centroid or center of these clusters….K Means Numerical Example.

Object | attribute 1 (X): weight index | attribute 2 (Y): pH |
---|---|---|

Medicine A | 1 | 1 |

Medicine B | 2 | 1 |

Medicine C | 4 | 3 |

Medicine D | 5 | 4 |

**How do you calculate K in K-means clustering?**

1. Elbow Curve Method

- Perform K-means clustering with all these different values of K. For each of the K values, we calculate average distances to the centroid across all data points.
- Plot these points and find the point where the average distance from the centroid falls suddenly (“Elbow”).

### How do you cluster a dataset in Python?

Steps:

- Choose some values of k and run the clustering algorithm.
- For each cluster, compute the within-cluster sum-of-squares between the centroid and each data point.
- Sum up for all clusters, plot on a graph.
- Repeat for different values of k, keep plotting on the graph.
- Then pick the elbow of the graph.

**How do you apply K-means clustering on a dataset?**

Introduction to K-Means Clustering

- Step 1: Choose the number of clusters k.
- Step 2: Select k random points from the data as centroids.
- Step 3: Assign all the points to the closest cluster centroid.
- Step 4: Recompute the centroids of newly formed clusters.
- Step 5: Repeat steps 3 and 4.

**Which statement is true for K-means clustering?**

Q. | Which statement is true about the K-Means algorithm? |
---|---|

B. | all attribute values must be categorical |

C. | all attributes must be numeric |

D. | attribute values may be either categorical or numeric |

Answer» c. all attributes must be numeric |

#### What is K-means and K Medoids clustering explain with help of an example?

Both the k -means and k -medoids algorithms are partitional (breaking the dataset up into groups). K-means attempts to minimize the total squared error, while k-medoids minimizes the sum of dissimilarities between points labeled to be in a cluster and a point designated as the center of that cluster.

**How do you find the centroid in K-means clustering example?**

Essentially, the process goes as follows:

- Select k centroids. These will be the center point for each segment.
- Assign data points to nearest centroid.
- Reassign centroid value to be the calculated mean value for each cluster.
- Reassign data points to nearest centroid.
- Repeat until data points stay in the same cluster.

**How do you apply k-means clustering on a dataset?**

## How do you interpret k-means clustering?

It calculates the sum of the square of the points and calculates the average distance. When the value of k is 1, the within-cluster sum of the square will be high. As the value of k increases, the within-cluster sum of square value will decrease.

**What is K-means clustering in machine learning?**

K-means clustering is the unsupervised machine learning algorithm that is part of a much deep pool of data techniques and operations in the realm of Data Science. It is the fastest and most efficient algorithm to categorize data points into groups even when very little information is available about data.

**What is Python clustering?**

Cluster analysis or clustering is an unsupervised machine learning algorithm that groups unlabeled datasets. It aims to form clusters or groups using the data points in a dataset in such a way that there is high intra-cluster similarity and low inter-cluster similarity.