Decomposition based feature recognition (DBFR) has drawn attention over the years. It has two stages: decomposition and aggregation. At the decomposition stage, the CAD model is partitioned into minimal cells. At the aggregation stage, the decomposed individual cells are composed in different combinations and these combinations are matched with predefined feature patterns to retrieve features in the model. The DBFR technique shows promises to deal with interactive features. However, DBFR algorithms suffer from the combinatorial problem in both the partitioning and the composing stages. This paper proposes a novel decomposition based feature recognition technique using the constrained and aggregated half-space partitioning. The constrained and aggregated half-space is defined in the occupation of a volume in the Euclidean space, bounded by multiple surfaces. The decomposition approach based on this concept can largely avoid over-cuttings. It tends to produce partitions that can be directly matched with feature patterns. Different from other DBFR algorithms, pattern matching is also introduced in the decomposition stage. Thus it further shrinks the space of combination and feature determination. Some algorithms are also proposed to do efficient volume combinations at the aggregation stage.