Anomaly Detection

Anomaly Detection
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Institution
Abstract
Outliers form a critical anomaly in research which has a detrimental influence on statistical tests conducted since the anomaly must be controlled to run any successful statistical analysis that can yield accurate results. The analysis incorporates important scientific methods which must be effectively considered so that the results obtained can be replicated. Thus, anomaly detection is carried out with a key focus in the systematic approach required to achieve higher level of success based on a given data analysis that is being considered in the study. Errors in data analysis limit he ability to employ key concepts which are key in improving the level of accuracy. Therefore, it is important to outline important elements which are essential in information management. Anomaly detection provide a critical evaluation in important elements which provide a strategic understanding on important elements which define change. Therefore, there is need to integrate important concepts which define positive outcomes. Better organization provide a strong focus on key elements which provide a strong level of commitment. The determination of outliers within a dataset help achieve better research evaluation. This paper provides a detailed analysis of essential elements that involve outlier detection and how to control the anomaly.
Anomaly detection provides a basis under which biases in data can be controlled and allow accurate analysis of the data available.

The goal of outlier detection
The primary purpose of outlier detection is to obtain accurate and unbiased data which can be used to conduct a statistical analysis. Conducting different statistical tests is based on various assumptions which must be met. Removing outliers is one of the assumptions that is evaluated to obtain accurate analysis results (Gupta et al., 2014).
Examples of outlier detection applications
Examples of outlier detection applications include supervised semi-supervised and unsupervised methods. Supervised methods define an outlier and a classification problem where experts are involved, and samples are examined. Semi-supervised methods where the number of labeled data is often small while unsupervised where normal objects are clustered and an outlier is expected to away from any groups of normal objects (Campos et al., 2016).
Types of outlier
A point outlier occurs when individual data instance can be considered as anomalous concerning the rest of the data. Contextual outlier occurs if a given data is inconsistent in a specific dataset. Collective outlier occurs if a particular group of data is inconsistent with the entire dataset (Gupta et al., 2014).
Outlier detection techniques
The main outlier detection techniques include numeric, z-score, isolation forest and DBScan. The numeric outlier is the simplest method which involves non-parametric outlier in a one-dimensional space. The outliers, in this case, are calculated based on the interquartile range analysis. Z -score assumes a Gaussian distribution of data, and thus the outliers are data points that occur in the tails of the distribution. DBScan is based on clustering method where the data points are considered as being either core points, border points or noise points. The noise points are identified as outliers. The isolation method focuses on a multi-dimensional feature involving large datasets (Campos et al., 2016).
References
Campos, G. O., Zimek, A., Sander, J., Campello, R. J., Micenková, B., Schubert, E., … & Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), 891-927.
Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), 2250-2267.