Episode mining is a research area in data mining, where the aim is to discover interesting
episodes, that is, subsequences of events, in an event sequence. The most popular episode …

Data mining is concerned with analysing large volumes of (often unstructured) data to
automatically discover interesting regularities or relationships which in turn lead to better …

Discovering contrasts between collections of data is an important task in data mining. In this
paper, we introduce a new type of contrast pattern, called a Minimal Distinguishing …

Abstract Machine Learning (ML) and Data Mining (DM) build tools intended to help users
solve data‐related problems that are infeasible for “unaugmented” humans. Tools need …

Frequent episode discovery is a popular framework for mining data available as a long
sequence of events. An episode is essentially a short ordered sequence of event types and …

摘要随着微博的风靡, 与之相关的研究得到学术界和工商界的广泛关注. 针对中文微博情感分析
的研究进行综述. 将中文微博文本情感分析分为三类任务: 文本预处理, 情感信息抽取和情感分类 …

We present a unifying view of temporal concepts and data models in order to categorize
existing approaches for unsupervised pattern mining from symbolic temporal data. In …

Episode Rule Mining is a popular framework for discovering sequential rules from event
sequential data. However, traditional episode rule mining methods only tell that the …

Discovering patterns in a sequence is an important aspect of data mining. One popular
choice of such patterns are episodes, patterns in sequential data describing events that …

An important goal of knowledge discovery is the search for patterns in data that can help
explain the underlying process that generated the data. The patterns are required to be new …