#### Filter Results:

- Full text PDF available (209)

#### Publication Year

1946

2017

- This year (6)
- Last 5 years (49)
- Last 10 years (92)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Cell Type

#### Data Set Used

#### Key Phrases

#### Method

#### Organism

Learn More

- Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth
- AI Magazine
- 1996

databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and… (More)

- David J. Hand, Heikki Mannila, Padhraic Smyth
- Drug safety
- 2001

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, and the aim is to model the shapes, or features of the shapes, of distributions. The other concerns small-scale, 'local' structures, and the aim is to detect… (More)

- Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth
- Advances in Knowledge Discovery and Data Mining
- 1996

We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple authors is… (More)

- Usama M. Fayyad, Padhraic Smyth, +22 authors Martin L. Kersten
- 1996

We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a… (More)

Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and this variety motivates the need for careful empirical… (More)

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 such as "a period of low telephone call activity is usually followed by a sharp rise ill call vohune". Examples of rules relating two or more time series are "if the… (More)

- Padhraic Smyth
- NIPS
- 1996

This paper discusses a probabilistic model-based approach to clustering sequences, using hidden Markov models (HMMs) . The problem can be framed as a generalization of the standard mixture model approach to clustering in feature space. Two primary issues are addressed. First, a novel parameter initialization procedure is proposed, and second, the more… (More)

- Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth
- Commun. ACM
- 1996

AS WE MARCH INTO THE AGE of digital information, the problem of data overload looms ominously ahead. Our ability to analyze and understand massive datasets lags far behind our ability to gather and store the data. A new generation of computational techniques and tools is required to support the extraction of useful knowledge from the rapidly growing volumes… (More)