Name
Abstract | ||
|---|---|---|
This study investigates the formulation of fuzzy logic as integrated component of the proposed model in data mining in order to classify the dataset prior to the implementation of data mining tools such summarization, association rule discovery, and prediction. The novel contribution of this paper is the fuzzification of the dataset prior to pattern discovery. The model is compared to the classical clustering, regression model, and neural network using the Internet usage database available at the UCI Knowledge Discovery on Databases (KDD) archive. Our test is anchored on parameters like relevant measure, processing performance, discovered rules or patterns and practical use of the findings. The proposed model indicates adequate performance in clustering, higher clustering accuracy and efficient pattern discovery compared with the other models. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11751632_97 | ICCSA (4) |
Keywords | Field | DocType |
data mining,adequate performance,discovering pattern,efficient pattern discovery,pattern discovery,classical clustering,fuzzy logic theory,regression model,data mining tool,association rule discovery,clustering accuracy,fuzzy logic,association rule,neural network,knowledge discovery | Data mining,Fuzzy clustering,Automatic summarization,Computer science,Fuzzy logic,Fuzzy set,Association rule learning,Knowledge extraction,Cluster analysis,K-optimal pattern discovery | Conference |
Volume | ISSN | ISBN |
3983 | 0302-9743 | 3-540-34077-7 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bobby D. Gerardo | 1 | 27 | 13.79 |
| Jaewan Lee | 2 | 62 | 14.66 |
| Su-Chong Joo | 3 | 105 | 13.18 |