What is Clustering?
It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples.
Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.
Need of Clustering
Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.
Grouping similar entities together help profile the attributes of different groups. The main advantage of clustering over classification is that, it is adaptable to changes and helps single out useful features that distinguish different groups. Clustering is also used to reduces the dimensionality of the data when you are dealing with a copious number of variables.
What does K-means Clustering mean?
K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity. The results of the K-means clustering algorithm are:
1.The centroids of the K clusters, which can be used to label new data
2. Labels for the training data (each data point is assigned to a single cluster)
How does K-means Clustering works?
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.
Real world use cases of K-means clustering in Security domain
cyber-profiling is the process of collecting data from individuals and groups to identify significant co-relations. the idea of cyber profiling is derived from criminal profiles, which provide information on the investigation division to classify the types of criminals who were at the crime scene. here is an interesting white paper on how to cyber-profile users in an academic environment based on user data preferences.
2)Call record detail analysis
a call detail record (cdr) is the information captured by telecom companies during the call, sms, and internet activity of a customer. this information provides greater insights about the customer’s needs when used with customer demographics. in this article , you will understand how you can cluster customer activities for 24 hours by using the unsupervised k-means clustering algorithm. it is used to understand segments of customers with respect to their usage by hours.
3)Rideshare data analysis
the publicly available uber ride information dataset provides a large amount of valuable data around traffic, transit time, peak pickup localities, and more. analyzing this data is useful not just in the context of uber but also in providing insight into urban traffic patterns and helping us plan for the cities of the future. here is an article with links to a sample dataset and a process for analyzing uber data.
4)Insurance fraud detection
machine learning has a critical role to play in fraud detection and has numerous applications in automobile, healthcare, and insurance fraud detection. utilizing past historical data on fraudulent claims, it is possible to isolate new claims based on its proximity to clusters that indicate fraudulent patterns. since insurance fraud can potentially have a multi-million dollar impact on a company, the ability to detect frauds is crucial. check out this white paper on using clustering in automobile insurance to detect frauds.
5)Delivery store optimization
optimize the process of good delivery using truck drones by using a combination of k-means to find the optimal number of launch locations and a genetic algorithm to solve the truck route as a traveling salesman problem. here is a whitepaper on the same topic .
K means clustering is one of the most popular clustering algorithms and usually the first thing practitioners apply when solving clustering tasks to get an idea of the structure of the dataset. The goal of k means is to group data points into distinct non-overlapping subgroups. It does a very good job when the clusters have a kind of spherical shapes and very useful in security domain.