K-Means Clustering Algorithm & it’s use cases

Here First we will get some basic overview about Clustering

Clustering -

• Clustering is the process of dividing the entire data into groups (also known as clusters) based on the patterns in the data.

• In clustering, we do not have a target to predict. We look at the data and then try to club similar observations and form different groups. Hence it is an unsupervised learning problem.

K-Means Clustering -

• K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.
• There is an algorithm that tries to minimize the distance of the points in a cluster with their centroid — the k-means clustering technique.
• The algorithm works as follows:

1. First we initialize k points, called means, randomly.
2. We categorize each item to its closest mean and we update the mean’s coordinates, which are the averages of the items categorized in that mean so far.
3. We repeat the process for a given number of iterations and at the end, we have our clusters.

Network Security Based on K-Means Clustering Algorithm -

• Nowadays, the network has become the basis of everything. Meanwhile, network security has become one of today’s most urgent social problem.

• Intrusion detection system is a system that can detect all software and hardware, and the application value is high. At present the system has already become the main network security management tool, can collect different set information in the system, and then combined with the function of the system of detection and automatic response
• Intrusion detection system is a behavior classifier, which operates through the judgment of information intrusion and non-invasive behavior. Here is the concept associated with intrusion detection.

• Data mining algorithm consists of cluster analysis algorithm, correlation analysis and classification algorithm. Clustering algorithms can be the object of the data set is divided into a lot of similar classes, and classification algorithm is similar, are complete data grouping .
• The application of unsupervised clustering algorithm in the field of abnormal detection can improve the detection efficiency of intrusion detection system and the practical application value is higher .
• K — means algorithm first determine the input parameters, the n in the sample data is divided into K class, the same data in a cluster similarity is high, the center of the cluster needs to be from the similarity of data in the group of the lower average.