@inproceedings{a1da011a4be04ad380425e1a875cbea4,
title = "Longitudinal Analysis of Economic Clusters: A Novel Methodology and Application of UK Regions",
abstract = "Standard Industrial Classification (SIC) classify organizations based on their business activities. However, choosing appropriate SIC code that represents an organization{\textquoteright}s business activities in a challenging task. In the UK, there are almost 100 categories each having several subcategories of predefined business activities designed by experts. However, such scheme cannot cater for emerging business needs while some organizations cannot be easily defined by a single SIC code, due to the complexity of their business nature. Similarly, if a company expands or changes its operation during the year, a new SIC code needs to be assigned. This results in organizations having difficulties picking representative SIC code to use in defining their business activities. In this paper, we propose a dynamic framework that can automatically group organizations based on their business activities. Our framework leverages techniques from topic modelling. Result shows that our proposed framework can automatically adapt to changing business needs and cluster organizations effectively.",
keywords = "Big data analytics, Longitudinal analysis, Standard industrial classification, Topic modelling",
author = "OLATUNJI, {Lyiola E.} and SEE-TO, {Wing Kuen Eric} and Savvas PAPAGIANNIDIS",
year = "2019",
month = dec,
language = "English",
volume = "2019-December",
series = "Proceedings of International Conference on Electronic Business (ICEB)",
publisher = "Association for Information Systems",
pages = "570--572",
editor = "LI, {Eldon Y.} and Honglei LI",
booktitle = "Proceedings of the 19th International Conference on Electronic Business : ICEB, Newcastle upon Tyne, U.K., December 8-12",
address = "United States",
note = "19th International Conference on Electronic Business, ICEB 2019 ; Conference date: 08-12-2019 Through 12-12-2019",
}