Triclustering in Big Data Setting
This work addresses computational efficiency for triclustering in big data settings, but it is incremental as it adapts existing algorithms rather than introducing new ones.
The paper adapted triclustering algorithms for efficient distributed computation using MapReduce and parallel programming, demonstrating good parallelization capabilities and providing performance and scalability comparisons.
In this paper, we describe versions of triclustering algorithms adapted for efficient calculations in distributed environments with MapReduce model or parallelisation mechanism provided by modern programming languages. OAC-family of triclustering algorithms shows good parallelisation capabilities due to the independent processing of triples of a triadic formal context. We provide the time and space complexity of the algorithms and justify their relevance. We also compare performance gain from using a distributed system and scalability.