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Discovery of rare association rules in the distribution of lawsuits in the federal justice system of Southern Brazil
- Lúcia Adriana Dos Santos Gruginskie,
- Guilherme Luís Roehe Vaccaro,
- Leonardo Dagnino Chiwiacowsky,
- Attila Elod Blesz Jr.
In the context of data mining, infrequent association rules may be beneficial for analysing rare or extreme cases with very low support values and high confidence. In researching risky situations or allocating specific resources, such rules may have a ...
Method for improvement of transparency: use of text mining techniques for reclassification of governmental expenditures records in Brazil
Many countries have transparency laws requiring availability of data. However, often data is available but not transparent. We present the Transparency Portal of Brazilian Federal Government case and discuss limitations of public acquisitions data stored ...
CARs-RP: Lasso-based class association rules pruning
Classification based on association rules gets more and more interest in research and practice. In many contexts, rules are often mined from sparse data in high-dimensional spaces, which leads to large number of rules with considerable containment and ...
A multiclass classification approach for incremental entity resolution on short textual data
Several web applications maintain data repositories containing references to thousands of real-world entities originating from multiple sources, and they continually receive new data. Identifying the distinct entities and associating the correct ...
A dynamic replicative K-means with self-compiling particle swarm intelligence for dataset classification
The classification techniques proposed so far is not sufficiently intelligent in classifying data set beyond two level classifications. To multi classify the data set for network data we are in need of more hybrid algorithms. In this paper we propose a ...
