Saeed Aghabozorgi

TL;DR: This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time- series approaches during the last decade and enlighten new paths for future works.

TL;DR: A comparative analysis of the systems based on market prediction based on online-text-mining expands onto the theoretical and technical foundations behind each and should help the research community to structure this emerging field and identify the exact aspects which require further research and are of special significance.

TL;DR: A technical framework is proposed to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms and should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.

TL;DR: A novel approach is proposed to predict intraday directional-movements of a currency-pair in the foreign exchange market based on the text of breaking financial news-headlines and produces a multi-layer algorithm that tackles each of the mentioned aspects of the text-mining problem at a designated layer.

TL;DR: The trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today's novel solutions are reviewed and the possible future path for more advanced algorithms is illuminated based on today’s available technologies and frameworks.