K-means Clustering and its applications in security domain
Clustering is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different. In other words, we try to find homogeneous subgroups within the data such that data points in each cluster are as similar as possible according to a similarity measure such as Euclidean-based distance or correlation-based distance. The decision of which similarity measure to use is application-specific.
Clustering is being used in Unsupervised Learning Algorithm in Machine Learning as it can be segregated multivariate data into various groups, without any supervisor, on basis of common pattern hidden inside the datasets.
What is unsupervised learning?
Unsupervised learning is where you train a machine learning algorithm, but you don’t give it the answer to the problem. In Unsupervised Learning, the machine uses unlabeled data and learns on itself without any supervision. The machine tries to find a pattern in the unlabeled data and gives a response.
What is k-means clustering?
K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster. The K-Means clustering algorithm is an iterative process where you are trying to minimize the distance of the data point from the average data point in the cluster.
The term ‘K’ is a number. You need to tell the system how many clusters you need to create. For example, K = 2 refers to two clusters. There is a way of finding out what is the best or optimum value of K for a given data.
How Does K-Means Clustering Work?
The flowchart below shows how k-means clustering works:
The goal of the K-Means algorithm is to find clusters in the given input data. There are a couple of ways to accomplish this. We can use the trial and error method by specifying the value of K (e.g., 3,4, 5). As we progress, we keep changing the value until we get the best clusters.
Another method is to use the Elbow technique to determine the value of K. Once we get the K’s value, the system will assign that many centroids randomly and measure the distance of each of the data points from these centroids. Accordingly, it assigns those points to the corresponding centroid from which the distance is minimum. So each data point will be assigned to the centroid, which is closest to it. Thereby we have a K number of initial clusters.
For the newly formed clusters, it calculates the new centroid position. The position of the centroid moves compared to the randomly allocated one.
How to choose the value of “K number of clusters” in K-means Clustering?
The performance of the K-means clustering algorithm depends upon highly efficient clusters that it forms. But choosing the optimal number of clusters is a big task. There are some different ways to find the optimal number of clusters, but here we are discussing the most appropriate method to find the number of clusters or value of K.
Where can we apply k-means?
k-means can typically be applied to data that has a smaller number of dimensions, is numeric, and is continuous. think of a scenario in which you want to make groups of similar things from a randomly distributed collection of things; k-means is very suitable for such scenarios.
Identifying crime localities
with data related to crimes available in specific localities in a city, the category of crime, the area of the crime, and the association between the two can give quality insight into crime-prone areas within a city or a locality.
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.
Cyber-profiling criminals
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.
Automatic clustering of it alerts
large enterprise it infrastructure technology components such as network, storage, or database generate large volumes of alert messages. because alert messages potentially point to operational issues, they must be manually screened for prioritization for downstream processes. clustering of data can provide insight into categories of alerts and mean time to repair, and help in failure prediction.
