This paper studies a new data mining problem called multiinstance outlier identification. This problem arises in tasks where each sample consists of many alternative feature vectors (instances) that describe it. This paper defines the multi-instance outliers and analyzes the basic types of multiinstance outliers. Two general identification approaches are proposed based on the state-of-the-art (...

For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commerce. Being an outlier, however, is not just a binary property. Instead, it is a property that applies to a certain degree to each object in a data set, depending on how ‘isolated’ this object is, with respect to the surr...

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...

Relict forests within tussock grassland and abundant subsoil charcoal indicate that prior to Polynesian conflagrations, conifer forests dominated by kaikawaka covered the upper montane plateaux in Moawhango Ecological Region. Much forest remains in peripheral districts where the plateaux border the encircling Kaimanawa, Kaweka and Ruahine greywacke uplands; in these districts, beech forest of t...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...

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...

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...