Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance in terms of predictive accuracy. This explains why, over the past decade, there have been many exten...

Three emotional intelligence models are widely discussed in the literature: the ability model, trait model, and mixed model. This study introduces a model for understanding emotional intelligence: the synergy model. The emotional intelligence model presented in this study is simple and easy to understand. This study suggests that emotional intelligence is the synergy present in intelligence, wh...

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 recently become an important problem in many data mining applications. In this paper, a novel unsupervised algorithm for outlier detection is proposed. First we apply a provably globally optimal Expectation Maximization (EM) algorithm to fit a Gaussian Mixture Model (GMM) to a given data set. In our approach, a Gaussian is centered at each data point, and hence, the estima...

Anomaly Detection is the process of finding outlying record from a given data set. This problem has been of increasing importance due to the increase in the size of data and the need to efficiently extract those outlying records which could indicate unauthorized access of the system, credit card theft or the diagnosis of a disease. The aim of this bachelor thesis is to implement a RapidMiner ex...

Local Outlier Factor (LOF) is an important and well known density based outliers handling algorithm, which quantifies, how much an object is outlying, in a given database. In this paper first we discuss LOF then we introduce the concept of ARDV. In LOF there is a concept of lrd (local reachability density). If in place of lrd we calculate ard (average reachability distance) and in place of LOF ...

An outlier is an observation that deviates so much from other observations that it seems to have been generated by a different mechanism. Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that exhibit a behavior that is very different from most of the individuals of the data set. Fr...

Unsupervised anomaly detection is the process of finding outlying records in a given dataset without prior need for training. In this paper we introduce an anomaly detection extension for RapidMiner in order to assist non-experts with applying eight different nearest-neighbor and clustering based algorithms on their data. A focus on efficient implementation and smart parallelization guarantees ...

anomaly recognition has always been a prominent subject in preliminary geochemical explorations. among the regional geochemical data processing, there are a range of statistical and data mining techniques as well as different mapping methods, which serve as presentations of the outputs. the outlier’s values are of interest in the investigations where data are gathered under controlled condition...