3,461 research outputs found
Outlier Detection from Network Data with Subnetwork Interpretation
Detecting a small number of outliers from a set of data observations is
always challenging. This problem is more difficult in the setting of multiple
network samples, where computing the anomalous degree of a network sample is
generally not sufficient. In fact, explaining why the network is exceptional,
expressed in the form of subnetwork, is also equally important. In this paper,
we develop a novel algorithm to address these two key problems. We treat each
network sample as a potential outlier and identify subnetworks that mostly
discriminate it from nearby regular samples. The algorithm is developed in the
framework of network regression combined with the constraints on both network
topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus
goes beyond subspace/subgraph discovery and we show that it converges to a
global optimum. Evaluation on various real-world network datasets demonstrates
that our algorithm not only outperforms baselines in both network and high
dimensional setting, but also discovers highly relevant and interpretable local
subnetworks, further enhancing our understanding of anomalous networks
Outlier Detection in Heterogeneous Datasets using Automatic Tuple Expansion
Rapidly developing areas of information technology are generating massive amounts of data. Human errors, sensor failures, and other unforeseen circumstances unfortunately tend to undermine the quality and consistency of these datasets by introducing outliers -- data points that exhibit surprising behavior when compared to the rest of the data. Characterizing, locating, and in some cases eliminating these outliers offers interesting insight about the data under scrutiny and reinforces the confidence that one may have in conclusions drawn from otherwise noisy datasets. In this paper, we describe a tuple expansion procedure which reconstructs rich information from semantically poor SQL data types such as strings, integers, and floating point numbers. We then use this procedure as the foundation of a new user-guided outlier detection framework, dBoost, which relies on inference and statistical modeling of heterogeneous data to flag suspicious fields in database tuples. We show that this novel approach achieves good classification performance, both in traditional numerical datasets and in highly non-numerical contexts such as mostly textual datasets. Our implementation is publicly available, under version 3 of the GNU General Public License
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
JSONoid: Monoid-based Enrichment for Configurable and Scalable Data-Driven Schema Discovery
Schema discovery is an important aspect to working with data in formats such
as JSON. Unlike relational databases, JSON data sets often do not have
associated structural information. Consumers of such datasets are often left to
browse through data in an attempt to observe commonalities in structure across
documents to construct suitable code for data processing. However, this process
is time-consuming and error-prone. Existing distributed approaches to mining
schemas present a significant usability advantage as they provide useful
metadata for large data sources. However, depending on the data source, ad hoc
queries for estimating other properties to help with crafting an efficient data
pipeline can be expensive. We propose JSONoid, a distributed schema discovery
process augmented with additional metadata in the form of monoid data
structures that are easily maintainable in a distributed setting. JSONoid
subsumes several existing approaches to distributed schema discovery with
similar performance. Our approach also adds significant useful additional
information about data values to discovered schemas with linear scalability
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