CAJ | 학술논문

基于聚类的孤立点检测算法得到的结果比较粗糙，不够准确。针对该问题，提出一种基于相似孤立系数的孤立点检测算法。定义相似距离以及相似孤立点系数，给出基于相似距离的剪枝策略，根据该策略缩小可疑孤立点候选集，并降低孤立点检测算法的计算复杂度。通过选用公共数据集 Iris、Labor和 Segment-test进行实验验证，结果表明，该算法在发现孤立点、缩小候选集等方面相比经典孤立点检测算法更有效。
기우취류적고립점검측산법득도적결과비교조조，불구준학。침대해문제，제출일충기우상사고립계수적고립점검측산법。정의상사거리이급상사고립점계수，급출기우상사거리적전지책략，근거해책략축소가의고립점후선집，병강저고립점검측산법적계산복잡도。통과선용공공수거집 Iris、Labor화 Segment-test진행실험험증，결과표명，해산법재발현고립점、축소후선집등방면상비경전고립점검측산법경유효。
Aiming at the problem that the result of outlier detection algorithm based on clustering is coarser and not very accurate, this paper proposes an outlier detection algorithm based on Approximate Outlier Factor(AOF). This algorithm presents the definition of the similarity distance and outlier similarity coefficient, and provides a pruning strategy based on similarity distance to reduce the suspect candidate sets to decrease the computational complexity. Experiments are carried out with public datasets Iris, Labor and Segment-test, and results show that the performance of detecting outlier and reducing candidate set of this algorithm is effective compared with the classical outlier detection algorithm.