Tsetlin machine (TM) is a logic-based machine learning approach with the
crucial advantages of being transparent and hardware-friendly. While TMs match
or surpass deep learning accuracy for an increasing number of applications,
large clause pools tend to produce clauses with many literals (long clauses).
As such, they become less interpretable. Further, longer clauses increase the
switching activity of the clause logic in hardware, consuming more power. This
paper introduces a novel variant of TM learning - Clause Size Constrained TMs
(CSC-TMs) - where one can set a soft constraint on the clause size. As soon as
a clause includes more literals than the constraint allows, it starts expelling
literals. Accordingly, oversized clauses only appear transiently. To evaluate
CSC-TM, we conduct classification, clustering, and regression experiments on
tabular data, natural language text, images, and board games. Our results show
that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the
accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After
the accuracy peaks, it drops gracefully as the clause size approaches a single
literal. We finally analyze CSC-TM power consumption and derive new convergence
properties.