In tracer kinetic studies for extracting biological information, measurement noise and error in the kinetics could affect the reliability of the estimated biological parameters. While the effect of statistical noise has been studied extensively before, the effect of outliers has not been addressed as much. In this study, we described an algorithm for detecting and removing outliers. Computer si...

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent item sets) fro...

In the power distribution system, missing or incorrect file of users-transformer relationship (UTR) in low-voltage station area (LVSA) will affect lean management LVSA, and operation maintenance network. To effectively improve paper proposes an identification method for UTR based on Local Selective Combination Parallel Outlier Ensembles algorithm (LSCP). Firstly, voltage data is reconstructed i...

T-distributed stochastic neighbor embedding (tSNE) is a popular and prize-winning approach for dimensionality reduction and visualizing highdimensional data. However, tSNE is non-parametric: once visualization is built, tSNE is not designed to incorporate additional data into existing representation. It highly limits the applicability of tSNE to the scenarios where data are added or updated ove...

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...

Identifying the genetic and ecological basis of adaptation is of immense importance in evolutionary biology. In our study, we applied a panel of 58 biallelic single nucleotide polymorphisms (SNPs) for the economically and culturally important salmonid Oncorhynchus keta. Samples included 4164 individuals from 43 populations ranging from Coastal Western Alaska to southern British Colombia and nor...

Identifying the genetic and ecological basis of adaptation is of immense importance in evolutionary biology. In our study, we applied a panel of 58 biallelic single nucleotide polymorphisms (SNPs) for the economically and culturally important salmonid Oncorhynchus keta. Samples included 4164 individuals from 43 populations ranging from Coastal Western Alaska to southern British Colombia and nor...

In cyber-physical systems sensors data should be anonymized at the source. Local data perturbation with differential privacy guarantees can be used, but the resulting utility is often (too) low. In this paper we contribute an algorithm that combines local, differentially private data perturbation of sensor streams with highly accurate outlier detection. We evaluate our algorithm on synthetic da...

Outlier detection is of great significance in the domain data mining. Its task to find those target points that are not identical most object generation mechanisms. The existing algorithms mainly divided into density-based and distance-based algorithms. However, both approaches have some drawbacks. former struggles handle low-density modes, while latter cannot detect local outliers. Moreover, o...

Internet of things (IoT) becomes the most popular term in recent advances Healthcare devices. The healthcare data IoT process and structure is very sensitive critical terms healthy technical considerations. Outlier detection approaches are considered as principal tool or stage any system mainly categorized statistical probabilistic, clustering classification-based outlier detection. Recently, f...