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Integrating Classification and Association Rule Mining
TLDR
This paper proposes a framework to integrate classification and association rule mining, called associative classification. Expand
Mining association rules with multiple minimum supports
TLDR
Association rule mining is an important model in data mining. Expand
XClust: clustering XML schemas for effective integration
TLDR
We introduce XClust, a novel integration strategy that involves the clustering of DTDs. Expand
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes
Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective To evaluate the performance of a DLS inExpand
Pruning and summarizing the discovered associations
TLDR
We propose a novel data mining technique that finds all associations in the data that satisfy the user specified minimum support and minimum confidence constraints and then finds a special subset of the unpruned associations to form a summary of the discovered associations. Expand
Supporting Frequent Updates in R-Trees: A Bottom-Up Approach
TLDR
We present a bottom-up update strategy for R-trees that generalizes existing update techniques and aims to improve update performance. Expand
Using General Impressions to Analyze Discovered Classification Rules
TLDR
We propose a technique that analyzes the discovered rules against a specific type of existing knowledge to help the user identify interesting rules. Expand
Mining relationships among interval-based events for classification
TLDR
In this paper, we augment the hierarchical representation with additional information to achieve a lossless representation of temporal patterns from interval-based events. Expand
Analyzing the Subjective Interestingness of Association Rules
TLDR
We propose an interestingness analysis system to help the user identify interesting association rules, in particular, expected and unexpected rules. Expand
DESIGN OF MUTANT OPERATORS FOR THE C PROGRAMMING LANGUAGE
TLDR
Mutation analysis is a method for reliable testing of large software systems. Expand
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