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Fast Discovery of Association Rules
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Principles of Data Mining
Data mining is the discovery of interesting, unexpected or valuable structures in large datasets. As such, it has two rather different aspects. One of these concerns large-scale, ‘global’ structures,Expand
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Discovery of Frequent Episodes in Event Sequences
Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in aExpand
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Random projection in dimensionality reduction: applications to image and text data
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however, empirical resultsExpand
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Levelwise Search and Borders of Theories in Knowledge Discovery
AbstractOne of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, findExpand
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Discovering Frequent Episodes in Sequences
Sequences of events describing the behavior and actions of users or systems can be collected in several domains. In this paper we consider the problem of recognizing frequent episodes in suchExpand
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Rule Discovery from Time Series
We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule suchExpand
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Discovering Generalized Episodes Using Minimal Occurrences
Sequences of events are an important special form of data that arises in several contexts, including telecommunications, user interface studies, and epidemiology. We present a general and flexibleExpand
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Computing Discrete Fréchet Distance ∗
The Fréchet distance between two curves in a metric space is a measure of the similarity between the curves. We present a discrete variation of this measure. It provides good approximations of theExpand
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The Discrete Basis Problem
Matrix decomposition methods represent a data matrix as a product of two factor matrices: one containing basis vectors that represent meaningful concepts in the data, and another describing how theExpand
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