Table of Contents

- 1 Can you use binary variables in KMeans?
- 2 Can you do clustering with categorical variables?
- 3 What is the best clustering algorithm for binary data?
- 4 Which type of data Cannot processed in K-means clustering?
- 5 What types of data can binary represent?
- 6 What is K prototype clustering?
- 7 Can I use binary variables in k-means?
- 8 What is the difference between mean and k means clustering?

## Can you use binary variables in KMeans?

For binary data, the Euclidean distance measure used by K-Means reduces to counting the number of variables on which two cases disagree. If all of the cluster variables are binary, then one can employ the distance measures for binary variables that are available for the Hierarchical Cluster procedure (CLUSTER command).

**Can you use categorical variables in K-means?**

This question seems really about representation, and not so much about clustering. Categorical data is a problem for most algorithms in machine learning. Suppose, for example, you have some categorical variable called “color” that could take on the values red, blue, or yellow.

### Can you do clustering with categorical variables?

KModes clustering is one of the unsupervised Machine Learning algorithms that is used to cluster categorical variables. KMeans uses mathematical measures (distance) to cluster continuous data. The lesser the distance, the more similar our data points are. Centroids are updated by Means.

**Can categorical data be binary?**

In statistics, binary data is a statistical data type consisting of categorical data that can take exactly two possible values, such as “A” and “B”, or “heads” and “tails”. Often, binary data is used to represent one of two conceptually opposed values, e.g: the outcome of an experiment (“success” or “failure”)

#### What is the best clustering algorithm for binary data?

Bernoulli Mixture model

A classic algorithm for binary data clustering is Bernoulli Mixture model. The model can be fit using Bayesian methods and can be fit also using EM (Expectation Maximization).

**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.

## Which type of data Cannot processed in K-means clustering?

Missing value Handling – k-Means clustering just cannot deal with missing values. Any observation even with one missing dimension must be specially handled. If there are only few observations with missing values then these observations can be excluded from clustering.

**What kind of clusters that K-means clustering algorithm produce?**

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.

### What types of data can binary represent?

Everything on a computer is represented as streams of binary numbers. Audio, images and characters all look like binary numbers in machine code. These numbers are encoded in different data formats to give them meaning, eg the 8-bit pattern 01000001 could be the number 65, the character ‘A’, or a colour in an image.

**Is categorical data qualitative or quantitative?**

Although categorical data is qualitative, it may sometimes take numerical values. However, these values do not exhibit quantitative characteristics. Arithmetic operations can not be performed on them. Categorical data may also be classified into binary and non-binary depending on its nature.

#### What is K prototype clustering?

K-Prototype is a clustering method based on partitioning. Its algorithm is an improvement of the K-Means and K-Mode clustering algorithm to handle clustering with the mixed data types. Read the full of K-Prototype clustering algorithm HERE. It’s important to know well about the scale measurement from the data.

**What does the K represent in K-means clustering?**

You’ll define a target number k, which refers to the number of centroids you need in the dataset. A centroid is the imaginary or real location representing the center of the cluster. Every data point is allocated to each of the clusters through reducing the in-cluster sum of squares.

## Can I use binary variables in k-means?

I need to use binary variables (values 0 & 1) in k-means. But k-means only works with continuous variables. I know some people still use these binary variables in k-means ignoring the fact that k-means is only designed for continuous variables.

**Is the k-means algorithm applicable to categorical data?**

The standard k-means algorithm isn’t directly applicable to categorical data, for various reasons. The sample space for categorical data is discrete, and doesn’t have a natural origin.

### What is the difference between mean and k means clustering?

K-Means clustering ignores model types (nominal and ordinal), and treat all numeric columns as continuous columns.” I just googled and got this. The point is mean is defined for continuous variables not for binary, so k means cannot use binary variables.

**Is it possible to use categorical variables in clustering?**

Since clustering relies on distances you can see how the feature with the smallest range contributes almost nothing when a distance is calculated. For number two: Yes you can use categorical variables by using a binary representation.