A rough set-based association rule approach implemented on exploring beverages product spectrum

[[abstract]]When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if—then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Data analytics e intelligenza artificiale per l\u2019analisi di bilancio. Performance e profili di business degli spin-off accademici

This research applies neural networks \u2013 namely: Self-Organising Maps (SOMs) - to analyse a bunch of financial indicators drawn from the balance sheet of academic spin-offs. The goal of the work is twofold: first, it aims at processing financial data to extract knowledge about the still uncertain role and strategic profile of academic spin-offs; and second, it aims at understating whether SOMs are able or not to support investigations on firms\u2019 performance, and to decide strategic orientation thanks to the processing of financial indicators. After a deep literature review about both the application of SOMs to financial reporting data and the business profile of academic spin-offs, the paper carries on an empirical investigation on 810 Italian academic spin-offs, using their financial reporting data. The results show that SOMs are able to extract the main features of different academic spin-off archetypes that can be then explained via traditional financial analysis instruments