K — mean clustering and its real use case in the security domain
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Every machine learning engineer hopes to achieve accurate predictions through their algorithms. These learning algorithms are generally divided into two types: supervised and unsupervised. K-means clustering is an unsupervised algorithm in which the available input data does not have labeled responses. Data mining is the appropriate field to apply on high volume crime dataset and knowledge gained from data mining approaches will be useful.
To perform crime analysis appropriate data mining approach need to be chosen and as clustering is an approach of data mining which groups a set of objects in such a way that object in the same group are more similar than those in other groups and involved various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Due to the sheer volume of inputs that are often involved in datamining problems, generic multiparty computation (MPC) protocols become infeasible in terms of communication cost. This has led to constructions of function-specific multiparty protocols that attempt to handle a specific functionality in an efficient manner, while still
providing privacy to the parties.
K mean clustering is implemented using open source data mining tool which are analytical tools used for analyzing data .Among the available open source data mining suite such as R, Tanagra ,WEKA ,KNIME ,ORANGE ,Rapid miner .k means clustering is done with the help of rapid miner tool
which is an open source statistical and data mining package written in Java with flexible data mining support options.
The vibrant nature of network traffic intrusions, unsupervised intrusion detection is more suitable for anomaly detection than classification-based intrusion detection methods.
