k-means clustering and its real use case in the security domain
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 Clustering ???
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.
K-Means Algorithm
K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering.
K-means algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster.
The way k-means algorithm works is as follows:
- Specify number of clusters K.
- Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
- Keep iterating until there is no change to the centroids. Example assignment of data points to clusters isn’t changing.
- Compute the sum of the squared distance between data points and all centroids.
- Assign each data point to the closest cluster (centroid).
- Compute the centroids for the clusters by taking the average of the all data points that belong to each cluster.
K-Means Use-Cases in the Security Domain
- 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.
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. We 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. 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 predictions.
4. Crime document classification-
Cluster documents in multiple categories based on tags, topics, and the content of the document. This is a very standard classification problem and k-means is a highly suitable algorithm for this purpose. The initial processing of the documents is needed to represent each document as a vector and uses term frequency to identify commonly used terms that help classify the document. the document vectors are then clustered to help identify similarity in document groups.
5. 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.
6. 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.
Thank You!!!
