Multiple analyses of a problem from diverse perspectives raise the chance that no relevant aspects of the problem will be ignored.In addition, as Internet applications become widespread, dealing with disparate data sources is becoming more and more popular.Technical constraints, security issues, and privacy considerations often limit access to some sources.Therefore, the concepts of collaborative computing intelligence and collaborative fuzzy modeling have been proposed, and certain so-called fuzzy collaborative systems are being established.In a fuzzy collaborative system, some experts, agents, or systems with various backgrounds are trying to achieve a common target.Since they have different knowledge and points of view, they may use various methods to model, identify, or control the common target.The key of such a system is that these experts, agents, or systems share and exchange their observations, settings, experiences, and knowledge each other when achieving the common goal.This features the fuzzy collaborative system distinct from the ensemble of multiple fuzzy systems.In the limited literature, fuzzy collaborative intelligence and systems have been successfully applied to collaborative clustering, group forecasting, agent negotiation, assessment and reputation, information filtering, robot control, intrusion detection, object tracking, etc. Applications of fuzzy collaborative intelligence in other fields remain to be investigated.Several studies have argued that for certain problems, a fuzzy collaborative intelligence approach is more precise, accurate, efficient, safe, and private than typical approaches.This special issue is intended to provide technical details of the development of fuzzy collaborative intelligence, fuzzy collaborative systems, and the corresponding applications.These details will hold great interest for researchers in information engineering, information management, artificial intelligence, and computational intelligence, as well as for practicing managers and engineers.This special issue

Clustering a group of large-scale time series with the same length is a frequently met problem in real world. However, the existing clustering methods often show high computational cost and low efficiency when dealing with this problem. In this paper, we propose a granulation-based horizontal collaborative fuzzy clustering method for this problem. In this method, some new subgroups are built from the given large-scale time series group by segmenting all the time series with time alignment. Thus, all the subsequences in each subgroup have the same length, which are much smaller than the length of the original time series. Just because of the smaller length, the clustering of all subsequences in each subgroup can be easily carried out with lower computation cost and higher efficiency. How to aggregate the clustering results of all subgroups to obtain the clustering result of the given group of large-scale time series becomes the main task of this paper. To solve this problem, we carry out the horizontal collaborative fuzzy clustering on the last subgroup by collaborating the clustering information of the previous subgroups. To obtain much better performance, we first perform fuzzy information granulation on the original group of large-scale time series. After that, the original group of time series is transformed into a group of granular time series. Therefore, the collaborative clustering is carried out on the corresponding granular subgroups. Simulation experiments presented here illustrate the good performance of the algorithm of our method.