A Genetic XK-Means Algorithm with Empty Cluster Reassignment

K-Means is a well known and widely used classical clustering algorithm. It is easy to fall into local optimum and it is sensitive to the initial choice of cluster centers. XK-Means (eXploratory K-Means) has been introduced in the literature by adding an exploratory disturbance onto the vector of cluster centers, so as to jump out of the local optimum and reduce the sensitivity to the initial centers. However, empty clusters may appear during the iteration of XK-Means, causing damage to the efficiency of the algorithm. The aim of this paper is to introduce an empty-cluster-reassignment technique and use it to modify XK-Means, resulting in an EXK-Means clustering algorithm. Furthermore, we combine the EXK-Means with genetic mechanism to form a genetic XK-Means algorithm with empty-cluster-reassignment, referred to as GEXK-Means clustering algorithm. The convergence of GEXK-Means to the global optimum is theoretically proved. Numerical experiments on a few real world clustering problems are carried out, showing the advantage of EXK-Means over XK-Means, and the advantage of GEXK-Means over EXK-Means, XK-Means, K-Means and GXK-Means (genetic XK-Means)

Occam's Razor For Big Data?

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor