What is K-Means Clustering?

As a data scientist in the making, you’ll learn various clustering algorithms and use them to analyze data more efficiently. One of these algorithms is called k-means, which can solve the complex data problems you’ll come across in your future career. But what is k-means? Read a definition of this term below and learn more about The Data Incubator’s data science programs. 

K-Means Definition

So what is k-means? Simply put, it’s a type of unsupervised machine learning algorithm that data scientists use when dealing with unlabeled data—data that does not have labels identifying its properties and classifications. Used in clustering, k-means divides large groups of data into smaller groups of data. 

Say you have a large data group of customers who purchase products from a store and you want to divide this group into smaller groups based on gender. K-means helps you do this. 

The letter “k” denotes the number of smaller groups created from a larger group. In the above example, when looking at gender, you divide the larger group into smaller groups of men and women, which equals k=2. 

Polish mathematician Hugo Steinhaus discovered the k-means algorithm in 1956. However, the term “k-means” first originated in an article by James MacQueen in 1967. Despite it being around for decades, successful data scientists still use k-means today, so it’s something you need to learn in a data science program. 

How Does K-Means Work?

As previously mentioned, k-means divides similar data items into smaller groups or “clusters.” This algorithm identifies similarities between data items for this process to happen. First, k-means finds and selects k-values. Then it initializes centroids, which are the real or imaginary locations representing a center of a cluster. Finally, k-means finds the average of the data items in a cluster and assigns it a new value.

There’s much more to k-means than this brief explanation. Enrolling in a data science program from TDI will help you learn this clustering algorithm and become a more proficient data scientist. Discover the TDI’s programs now. 

Benefits of K-Means? 

Here are some of the advantages of k-means:

Easy to Understand

Once you’ve learned it, k-means is an easy-to-understand algorithm that will help you make sense of enormous datasets. As previously mentioned, data scientists use k-means to analyze unlabeled data.

Used Across Various Business Verticals

Data scientists can use k-means for various business use cases in different sectors. For example, it helps marketing companies segment audiences into smaller targeted groups and send them personalized marketing materials. 

Flexibility

You can use the k-means algorithm when analyzing categorical and continuous data, making it a good choice for different data projects.

Speed

K-means is a fast algorithm that won’t slow down data analysis, even when analyzing large data sets. 

Drawbacks of K-Means? 

Disadvantages of k-means include:

• This algorithm can be sensitive to data outliers, which can skew the outcomes of data analysis. As a data scientist, you need to identify outliers before using any clustering algorithm.
• When the number of dimensions used in the k-means increases, it becomes more difficult to scale data. 
• K-means might struggle to cluster data when clusters are large and dense. In these scenarios, data scientists have to “generalize” k-means.