There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

The complete mitochondrial genome (mitogenome) of Diaphania pyloalis Walker collected from China was reported and characterized. The mitogenome was 14,960 bp in length, including 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes and 1 short A + T-rich region. The A + T content of the mitochondrial genome is 80.77%. All protein-coding genes were initiated by an ATN codon, except for coxI gene which is initiated by CGA. Only coxII gene was terminated with a single T. There are 13 overlaps totaling 52 bp, and 13 intergenic spacer regions totaling 121 bp in the D. pyloalis mitogenome. The short A + T-rich region is 67 bp long, with 91.04% A + T content.