10 Interesting Use Cases for the K-Means Algorithm

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The K-means algorithm has a long history and is also one of the most commonly used clustering algorithms. The K-means algorithm is very simple to implement and therefore, it is very suitable for novice machine learning enthusiasts. Let us first review the origins of the K-Means algorithm and then introduce its more typical application scenarios.

originate

The term "K-means" was first introduced by James MacQueen in 1967 in his paper "Some Methods for the Classification and Analysis of Multivariate Observations". In 1957, Bell Labs also used standard algorithms for pulse-coded modulation techniques. In 1965, E.W. Forgy published what is essentially the same algorithm, the Lloyd-Forgy algorithm.

What is the K-Means algorithm?

Clustering, is the division of data into groups such that data points in the same group are more similar to each other than to data points in other groups. In short, clustering is the partitioning of data points with similar characteristics into groups, that is, in clusters. The goal of the K-means algorithm is to find an individual group in the data and the number of groups is represented by the variable K. Each data point is assigned to one of the K groups by iterative operations based on the features provided by the data. K = 2 in the figure below, so two clusters can be identified from the original dataset.

The K-means algorithm is executed on a dataset with the following outputs, respectively.

1.K centroids: the centroids of each of the k clusters identified from the dataset.

2.The dataset is fully labeled to ensure that each data point can be assigned to one of the clusters.

Top 10 Use Cases for the K-Means Algorithm

The K-means algorithm can usually be applied to data sets that are small in dimension and value and continuous, e.g., grouping identical things from a randomly distributed set of things.

1.Document Classifier

Divides documents into several different categories based on tags, topics and document content. This is a very standard and classical K-means algorithm classification problem. First, it is necessary to initialize the documents by representing each document as a vector and using term frequencies to identify common terms for document classification, which is a necessary step. The document vectors are then clustered to identify similarities in groups of documents. Here is the case of K-means algorithm implementation for document classification.

2.Item transfer optimization

A combination of K-means algorithm is used to find the optimal launch location of the drone and genetic algorithm to solve the traveler's route of travel problem and optimize the drone item transfer process. Here's the white paper on the project.

3.Identification of crime sites

Using relevant crime data for specific areas of the city, analyzing crime categories, crime locations, and the correlation between the two, a high-quality survey of crime-prone areas of the city or region can be done. This is a paper based on crime data from the Delhi FIR.

4.Customer Classification

Clustering can help marketers improve their customer base (working within their target area) and further segment customer categories based on their purchase history, interests or activity monitoring. This is a whitepaper on how telecom operators divide prepaid customers into categories such as recharge mode, sending SMS and browsing websites. Segmenting customers helps companies to develop specific ads for specific customer segments.

5.Team Form Analysis

Analyzing a player's form has always been a key element in sports. With increasing competition, machine learning is playing a crucial role in this field as well. The K-means algorithm is a good choice if you want to create a good team and like to identify similar players based on their status. Please refer to this article for specific details and implementation.

6.Insurance fraud detection

Machine learning also plays a crucial role in fraud detection and is widely used in the areas of automotive, health insurance and insurance fraud detection. Historical data from previous fraudulent claims is used to identify new claims based on its similarity to fraudulent pattern clustering. Fraud detection is critical for companies because insurance fraud can cost them millions of dollars. This is a white paper on the use of clustering in auto insurance to detect fraud.

7.Ridership data analysis

The dataset of Uber ride information that is publicly available to the general public provides us with a large number of valuable datasets about traffic, transit times, peak ride locations, and more. Analyzing this data will not only be of great benefit to Uber, but it will help us gain insight into the transportation patterns of our cities to help us plan for their future. This is an article that uses a single sample dataset to analyze the Uber data process.

8.Cyber analysis of criminals

Network analysis is the process of collecting data from individuals and groups to identify important relationships between the two. The network analysis stems from the crime file, which provides information from the investigation department to classify offenders at the crime scene. This is a paper on how to cyber-profile web users based on user data preferences in an academic setting.

9.Detailed analysis of call records

Call Detail Records (CDR) is the collection of information by telecommunication companies on the calls, SMS and network activities of their subscribers. Combining call detail records with customer profiles can help telecom companies make more predictions about customer needs. In this article, you'll learn how to use an unsupervised K-Means clustering algorithm to cluster a customer's activity over a 24-hour day to understand their usage over several hours.

10.Automated clustering of IT alerts

Large enterprise IT infrastructure technology components (such as network, storage or database) generate a large number of alert messages. Since alert messages can be directed to specific operations, the alert messages must be manually filtered to ensure that subsequent processes are prioritized. Clustering the data provides insight into alert categories and average repair times, which can help in predicting future failures.