ignoring two main characteristics of the concentration-response data, correlation between observations and presence of outliers, may lead to misleading results. therefore the special method should be considered. in this paper in to examine the effect of phenylephrine in rat corpus cavernosum, outlier robust nonlinear mixed estimation is used. in this study, eight different doses of phenylephrin...

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...

Despite computation becomes much complex on data with unprecedented large-scale, we argue computers or smart devices should and will consistently provide information and knowledge to human being in the order of a few tens milliseconds. We coin a new term 10-millisecond computing to call attention to this class of workloads. Public reports indicate that internet service users are sensitive to th...

Summarization requires selection of the more informative sentences within a set of documents. Generally, process assumes the document set includes related topics to a subject. However, some of the documents may be outlier and the effect of an outlier document might affect the success of extractive summary. Research is focused on filtering documents at the extraction stage these are outlier. Ext...

detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (wsns). to address the problem of outlier detection in wireless sensor networks, in this paper we present a pca-based centralized approach and a dpca-based distributed energy-efficient approach for detecting outliers in sensed data in a wsn. the outliers in sensed data can be ca...

Ignoring two main characteristics of the concentration-response data, correlation between observations and presence of outliers, may lead to misleading results. Therefore the special method should be considered. In this paper in to examine the effect of phenylephrine in rat Corpus cavernosum, outlier robust nonlinear mixed estimation is used. in this study, eight different doses of phenylephrin...

Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The se...

Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The se...

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detec...

Outlier detection has been a long-debated subject among researchers due to its effect on model fitting. Spatial outlier received considerable attention in the recent past. On other hand, accommodation, particularly spatial applications, retains vital information about model. It is pertinent develop method that capable of accommodating detected outliers fashion models. In this paper, we formulat...