K means clustering is a form of unsupervised learning. Data scientists use it when they have loads of unlabeled data (any info without defined groups or categories.) K means clustering’s goal is to search for data for various groups. The alphabetic variable “K” should represent each group. Contrary to several algorithms, this one works repeatedly to assign every data point to a K group while considering the features at its disposal.

Data points become clustered based on the similarity of their features. Here are some results of K means clustering algorithm:

  • You can use the center of K clusters for labeling new data.
  • Every cluster has the training data labels assigned to it.

Instead of defining groups before studying the data, this algorithm enables you to search and comprehend the organically formed groups. You will find a section titled “Selecting K” in this article. It describes how you can determine the number of groups.

Every cluster’s centroid is a collection of values defining the resulting groups. Analyzing the centroid feature can help to interpret the type of group represented by each cluster.

Business Uses of K Means Clustering

Data scientists use K means clustering algorithm to identify groups lacking data labels. The procedure helps confirm various business-related assumptions regarding the types of existing groups. You can also use it to discover unknown groups from elaborate data sets. Once the algorithm completes its process and defines the group, you can easily assign new data to its respective group. K means clustering is a highly versatile algorithm that helps with virtually every grouping type. Here are some examples:

Spotting Anomalies or Bots

  • Separate bots from useful activity groups
  • The algorithm helps to clear outlier detection through group valid activity

Classifying Sensor Measurements

  • Detecting group photos
  • Identifying health monitoring groups
  • Separating audio
  • Detecting various activities in motion sensors

Inventory Categorization

  • Group inventory with manufacturing metrics
  • Group inventory with sales activity

Behavioral Segmentation

  • Defining interest-based personas
  • Creating  activity monitoring based profiles
  • Segmenting with by utilizing purchase history
  • Creating segments by judging activities on platforms, websites, and applications

Understanding the Algorithm

K means clustering algorithm produces final results by utilizing iterative refinement techniques. The data set and the number of clusters is the algorithm inputs. Also, the data set is a group of features for every data point. As discussed earlier, the algorithm begins with K centroid’s initial estimates. They can be randomly selected or randomly generated from the set. The algorithm then repeats the following steps.

Data Assignment Step

Every centroid determines a cluster. In this procedure, each data point is allotted to its closest centroid cased on its Euclidean distance.

Centroid Update Step

The algorithm recomputes the centroids in this step. It does so by collecting the mean of each data point allotted to the centroid’s cluster.

The algorithm repeats steps one and two until meeting a stopping criterion. The K means clustering algorithms guarantee accurate results. However, the outcome obtained sometimes may not be the most desirable. Analyzing multiple algorithms with random starting centroids could provide a better outcome.

Selecting K

K means clustering’s primary purpose is to find data set labels and clusters for specific pre-chosen Ks. So, users must run this algorithm for a wide variety of K values and analyze each result side by side to determine the number of data clusters. There is no particular method to determine Ks exact value. However, you can still obtain accurate estimates by using the techniques mentioned below.

A popular metric commonly used for comparing results across numerous K values is the mean distance in the middle of the cluster centroid and its data points. Since increasing clusters will minimize the distance between data points, raising the number of clusters will reduce data points’ distance each time. Expanding K will decrease the metric and could make it as low as zero as long as K is similar to the amount of data points.

So, you cannot use this metric as a single target. Alternatively, you can plot the centroid’s mean distance as K’s function, where the decrease rate shifts sharply. It could provide you a rough answer to K. Numerous other techniques could help you validate K. Here is a list of some popular methods used by experienced data scientists.

  • G-means algorithm
  • The silhouette method
  • Theoretic jump information method
  • Information criteria
  • Cross-validation method

Additionally, observing data point distribution across various groups offers valuable insight into how the algorithm splits data for Ks.

The Role of Feature Engineering in K Means Clustering

Feature engineering is a process where you utilize domain knowledge to select accurate data metrics. People use feature engineering to determine the correct metrics to feature in their machine learning algorithms. It would be fair to claim that this engineering-type plays a critical part in the K means clustering algorithm. It helps you distinguish naturally occurring sets with little to no hassle.

Categorical data like browser types, countries, gender must be separated or encoded in a way that blends well with the algorithm. Feature transformations are especially helpful for representing rates instead of measurements. It is vastly helpful for normalizing data.

K Means Clustering Real World Applications

K means clustering is becoming increasingly popular in various industries. Here are some popular real-world applications of this revolutionary algorithm.

Recommendation Engines

Clustering is quite beneficial for recommendation engines. You can take advantage of this algorithm and recommend songs or movies to your friends based on their preferences.

Image Segmentation

K means clustering is excellent for segmenting photos. Illustration and editing programs can benefit from this algorithm’s image segmenting attributes.

Document Clustering

Clustering can help you group numerous documents in little to no time. It is particularly helpful for people possessing multiple documents containing different pieces of information.

Customer Segmentation

Numerous industries use K means clustering’s customer segmenting qualities to streamline their processes. Sales, advertising, sports, e-commerce, banking, and telecommunication are some fields that take advantage of this algorithm.