Irregular applications that involve indirect memory accesses were traditionally considered unsuitable for SIMD processing. Though some progress has been made in recent years, the existing approaches require either expensive data reorganization or favorable input distribution to deliver good performance. In this work, we propose a novel vectorization approach called in-vector reduction that can efficiently accelerate a class of associative irregular applications. This approach exploits associativity in the irregular reductions to resolve the data conflicts within SIMD vectors. We implement in-vector reduction with the new conflict detecting instructions that are supported in Intel AVX-512 instruction set and provide a programming interface to facilitate the vectorization of such associative irregular applications. Compared with previous approaches, in-vector reduction eliminates a large part of the overhead of data reorganization and achieves high SIMD utilization even under adverse input distributions. The evaluation results show that our approach is efficient in vectorizing a diverse set of irregular applications, including graph algorithms, particle simulation codes, and hash-based aggregation. Our vectorization achieves 1.5x to 5.5x speedups over the original sequential codes on a single core of Intel Xeon Phi and outperforms a competing approach, conflict-masking based vectorization, by 1.4x to 11.8x.