Use of the fuzzy clustering algorithm for pattern recognition in feed consumption data of pure New Zealand white rabbits exposed to varied thermal challenges

Abstract

The performance of New Zealand White rabbits (NZW) is directly associated with to ambiance-related factors because they present high sensitivity to high-temperature conditions. The objective of the present work was to use the Fuzzy C-Means (FCM) clustering algorithm for pattern recognition in daily feed consumption (CDR) of NZW rabbits exposed to different thermal challenges. The experiment was carried out in four air-conditioned wind tunnels installed in a laboratory. Twenty-four pure rabbits of the NZW breed aged 30 to 37 days were used. The experiment was carried out in two stages with a period of seven days each, and, at each stage, four dry bulb temperatures (20°C, 24ºC, 28ºC and 32ºC) were tested from the 30th day of the rabbits’ life. Data on CDR (kilo, kg day-1) were obtained by weighing the quantities supplied and the leftovers obtained daily from each rabbit in each treatment. Afterward, the Fuzzy C-Means algorithm (FCM) was used to classify the results. Also, to validate the analysis, the validation indexes were applied to indicate in which quantities of clusters the best partition results were obtained for this database. Thus, FCM cluster analysis was set up as a methodology capable of providing information on the thermal comfort of NZB rabbits in a precise and non-invasive way, which could assist the producer in decision-making.