Detecting outliers in mixed attribute datasets is one of major challenges in real world applications. Existing outlier detection methods lack effectiveness for mixed attribute datasets mainly due to their inability of considering interactions among different types of, e.g., numerical and categorical attributes. To address this issue in mixed attribute datasets, we propose a novel Pattern based ...

We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for (OD) in functional data. FastMUOD detects outliers by computing, each curve, an amplitude, magnitude, shape index meant to target corresponding types outliers. Some methods adapting multivariate data are then proposed. These include applying on components using random projection...

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...

Outlier detection, a data mining technique to detect rare events, deviant objects, and exceptions from data, has been drawing increasing attention in recent years. Most existing outlier detection algorithms focus on numerical data sets. We target categorical record databases and detect records in which many attribute values are not observed even though they should occur in association with othe...

In this paper, we describe the development and evaluation of a core algorithmic component for robust robotic planetary surface mapping. In particular, we consider the issue of outlier measurements when utilizing both odometry and sparse features for laser scan alignment. Due to the heterogeneity of the measurements and the relative scarcity of distinct geometric features in the planetary enviro...

We study a novel problem lying at the intersection of two areas: multi-armed banditand outlier detection. Multi-armed bandit is a useful tool to model the processof incrementally collecting data for multiple objects in a decision space. Outlierdetection is a powerful method to narrow down the attention to a few objects afterthe data for them are collected. However, no one has st...

In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting nonsmooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very ...