of a thesis at the University of Miami. Thesis supervised by Professor Dilip Sarkar. No. of pages in text. (55) Aggregation of data from multiple sensor nodes is usually done by simple methods such as averaging or, more sophisticated, iterative filtering methods. However, such aggregation methods are highly vulnerable to malicious attacks where the attacker has knowledge of all sensed values an...

Error in data is one of the facts that cause the parameter estimations to be subjective. If the erroneous case is proved statistically, then these cases are called outliers. Outliers are defined as the few observations or records which appear to be inconsistent with the rest of the group of the sample and more effective on prediction values. Isolated outliers may also have positive impact on th...

Of all the data mining techniques, outlier detection seems closest to the definition of “discovering nuggets of information” in large databases. When an outlier is detected, and determined to be genuine, it can provide insights, which can radically change our understanding of the underlying process. The purpose of the research underlying this thesis was to investigate and devise methods to mine...

Outlier detection is a task that finds objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data. Outlier detection has wide applications which include data analysis, financial fraud detection, network intrusion detection and clinical diagnosis of diseases. In data analysis applications, outliers are often considered as error or noise and are remov...

Among the many challenges posed by huge data volumes produced new generation of astronomical instruments there is also search for rare and peculiar objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare performances six methods: Local Outlier Factor, Isolation Forest, k-means clustering, measure novelty, both normal convolutional autoencoder. T...

We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipm...

This work presents an adaptive outlier detection technique for data streams, called Automatic Outlier Detection for Data Streams (A-ODDS), which identifies outliers with respect to all the received data points (global context) as well as temporally close data points (local context) where local context are selected based on time and change of data distribution.

A novel approach to outlier detection on the ground of the properties of distribution of distances between multidimensional points is presented. The basic idea is to evaluate the outlier factor for each data point. The factor is used to rank the dataset objects regarding their degree of being an outlier. Selecting the points with the minimal factor values can then identify outliers. The main ad...

Frequent pattern outlier factor is used to detect outliers with complete frequent itemsets. But it is difficult in real-world time-series data streams application because of its low efficiency. In this paper, we propose a novel maximal frequent pattern outlier factor (MFPOF) and an outlier detection algorithm (OODFP) for online high-dimensional time-series outlier detection. Firstly, the time-s...