Between the dawn of the Internet through year 2003, there were just a few dozens exabytes of information on the Web. Today, that much information is created weekly. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised increasing interest both in the scientific community...

The Consumer Expenditure Quarterly Interview Survey collects data from consumer units (CUs) about their expenses during the previous 3 months. The purpose of the survey is to gather information about large purchases, such as those of vehicles and appliances, and expenditures that are made on a regular basis, such as rent and utility payments. These data are collected by the U.S. Census Bureau a...

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. I...

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...

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspacebased outlier detection methods identify outliers by searching for abnormal sparse density units in s...

Abstract This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts network revenue management. Such management, e.g. railway itineraries, needs accurate forecasts. However, across or parts a complicate forecasting, and the structure makes such hard detect. We propose two-step combining clustering with functional outlier detection ide...