An Efficient Tree based algorithm for Association Rule Mining

Abstract

Data mining is a process by which the from raw data information and important patterns are estimated. That involves the intermediate processes to find the data patterns from the input datasets. These processes are preprocessing, algorithm implementation and the testing of developed model. algorithm are used find the data pattern from the data, that may in form of associative, any data structure based or weight based. The proposed work is an effort in order to develop an association rule mining algorithm using the decision tree. Where the decision tree is used first to find the decision rules and according to rules new associate rules are extracted from the data.

Application of biclustering algorithm to extract rules from labeled data

“…Feature selection is usually a necessary step to facilitate further processing which is especially true for high dimensional data (Gorzałczany and Rudzi nski, 2017). In machine learning and statistics, attribute selection is the process of selecting a subset of the fewest number of informative attributes for classification, rule extraction and the other applications (Shrivastava and Barua, 2015).…”

“…Rule-based expert systems are often applied to classification problems in various application fields, like fault detection, biology and medicine (Dahal et al , 2015; Shrivastava and Barua, 2015). In Roubos et al ’s (2003) study, the authors show a compact, accurate and interpretable fuzzy rule-based classifiers obtained from labeled observation data.…”