Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Phishing Web Page Detection using Optimised Machine Learning

Phishing is a type of social engineering attack that can affect any company or anyone. This paper explores the effect that different features and optimisation techniques have on the accuracy of intelligent phishing detection using machine learning algorithms. This paper explores both hyperparameter optimisation as well as feature selection optimisation. For hyperparameter tuning, both TPE (Tree-structured Parzen Estimator) and GA (Genetic Algorithm) were tested, with the best option being model dependent. For feature selection, GA, MFO (Moth Flame Optimisation) and PSO (Particle Swarm Optimisation) were used with PSO working best with a Random Forest model. This work used URL (Uniform Resource Locator), DOM (Document Object Model) structure, page rank and page information related features. This research found that the best combination was Random Forest using PSO for feature selection and TPE for hyperparameter optimisation, giving an accuracy of 99.33%