Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data. Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have been proposed to overcome the problem of outlier detection in spatial Geogr...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, Fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different to the most of the individuals of the...

Outlier detection is an important task in data mining and its applications. It is defined as a data point which is very much different from the rest of the data based on some measures. Such a data often contains useful information on abnormal behavior of the system described by patterns. In this paper, a novel method for outlier detection is proposed among inconsistent dataset. This method expl...

In many application areas, it is important to detect outliers. The traditional engineering approach to outlier detection is that we start with some “normal” values x1, . . . , xn, compute the sample average E, the sample standard variation σ, and then mark a value x as an outlier if x is outside the k0-sigma interval [E − k0 · σ, E + k0 · σ] (for some pre-selected parameter k0). In real life, w...

Speeding describes the unusually fast responses provided to survey questions. A characteristic of speeders is that answers by-pass cognitive process. Consequently, this low respondent engagement results in the poor quality and validity of data. The issue at hand is how to detect speeders in a survey. The presumption is the use of different statistical outlier detection methods. This paper prese...

The discrete data structure and large sequencing depth of RNA sequencing (RNA-seq) experiments can often generate outlier read counts in one or more RNA samples within a homogeneous group. Thus, how to identify and manage outlier observations in RNA-seq data is an emerging topic of interest. One of the main objectives in these research efforts is to develop statistical methodology that effectiv...

Outlier detection is an integral part of robust evaluation for crowdsourceable Quality of Experience (QoE) and has attracted much attention in recent years. In QoE for multimedia, outliers happen because of different test conditions, human errors, abnormal variations in context, etc. In this paper, we propose a simple yet effective algorithm for outlier detection and robust QoE evaluation named...

‘‘One person’s noise is another person’s signal” (Knorr, E., Ng, R. (1998). Algorithms for mining distancebased outliers in large datasets. In Proceedings of the 24th VLDB conference, New York (pp. 392–403)). In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers – objects which behave in an unexpected way or have abnormal properties....

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different from the most of the individuals of t...

Multi-label learning is a challenge problem in computer vision fields. Since annotating a multilabel instance costs greatly, multi-label classification has become a hot topic research. State-of-theart active learning methods either annotate all the relevant samples without diagnosing discriminative information in the labels or annotate only limited discriminative samples manually, that has weak...