Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Nowadays, with the advance of technology, many applications generate huge amounts of
data streams at very high speed. Examples include network traffic, web click streams, video …

We introduce RealTime QA, a dynamic question answering (QA) platform that announces
questions and evaluates systems on a regular basis (weekly in this version). RealTime QA …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Twitter is among the fastest‐growing microblogging and online social networking services.
Messages posted on Twitter (tweets) have been reporting everything from daily life stories to …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

As recently as the 1990s, studies showed that most people preferred getting information
from other people rather than from information retrieval systems. Of course, in that time …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

This article describes the latest development of a generic approach to detecting and
visualizing emerging trends and transient patterns in scientific literature. The work makes …