Which of the following machine learning techniques helps in detecting the outliers in data?
Question
Which of the following machine learning techniques helps in detecting the outliers in data?
Solution
There are several machine learning techniques that can help in detecting outliers in data. Here are a few:
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Z-Score: This statistical measurement describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. If the Z-Score is far from zero, it indicates that the data point is far from the mean and thus, it is an outlier.
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IQR (Interquartile Range): The IQR is a measure of statistical dispersion, or in other words, it is a measure of overall data spread. It is very useful in detecting outliers. Any data points that fall below Q1 – 1.5 IQR or above Q3 + 1.5 IQR are considered outliers.
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Isolation Forest: This is a machine learning algorithm for anomaly detection. It's based on modeling the normal data in such a way as to isolate anomalies that are both few in number and different in the feature space.
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DBSCAN (Density-Based Spatial Clustering of Applications with Noise): This is a density-based clustering algorithm, which can be used to detect the outliers. In DBSCAN, a data point is an outlier if it is not part of a cluster.
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Local Outlier Factor (LOF): This is a density-based outlier detection method, which considers the local density deviation of a given data point with respect to its neighbors. It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood.
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One-Class SVM: This is an unsupervised algorithm that learns a decision function for novelty detection, i.e., classifying
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Data mining is a process of extracting valid, previously unknown, and ultimately comprehensible information from large datasets and using it for organizational decision making [10]. However, there a lot of problems exist in mining data in large datasets such as data redundancy, the value of attributes is not specific, data is not complete and outlier [13].Outlier is defined as an observation that deviates too much from other observations that it arouses suspicions that it was generated by a different mechanism from other observations [21]. The identification of outliers can provide useful, sufficient and meaningful knowledge and number of applications in areas such as climatology, ecology public health, transportation, and location based services. Recently, a few studies have been conducted on outlier detection for large dataset [4]. However, most existing study concentrate on the algorithm based on special background, compared with outlier identification approach is comparatively less. This paper mainly discusses about outlier detection approaches from data mining perspective. The inherent idea is to research and compare achieving mechanism of those approaches to determine which approach is better based on special dataset and different background. if nothing seems compatible or relevant just tell me
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