Conceptual Model of Incremental R-Eclat Algorithm for Infrequent Itemset Mining

Abstract

Mining valuable information from database could be very challenging especially for crucial decision-making. This is because mining association rule may require repetitious scanning of large databases that leads to the use of high memory usage and affects the running time. Few methods and algorithms were introduced by researchers to handle the issues in data mining. Rare Equivalence Class Transformation (R-Eclat) algorithm is one of the rule mining techniques using vertical format data repositories for infrequent pattern mining. The main operation in R-Eclat is intersecting tidset. Since the size of tidsets would affect the memory usage and its running time, more memory and time required for a bigger tidsets. Adopting to R-Eclat algorithm, a new incremental approach is introduced called Incremental Rare Equivalence Class Transformation (IR-Eclat). IR-Eclat specifically design for infrequent pattern mining and it is beneficial for dynamic database as the data is increasing in volume from time to time. In conjunction with big data explosion, the end users are at an advantage for the use of this incremental approach.

Efficient Algorithms for Patterns Identification in Medical Data

“…The algorithm uses the depthfirst search method with the advantage of the vertical layout for the database representation, where each item is denoted by a set of transaction IDs (called a tidset). The algorithm is implemented by intersecting itemset elements, and no counting support is needed, since the support of an itemset is annotated by the size of the itemset [1].…”

“…This approach is applicable to both sparse and dense databases [13]. • Postdiffset: The postdiffset approach starts with the tidsets process for the first level loop, then moves to the diffset approach for the second level forward [1].…”

“…These new features include adding a filter to the null or empty value removed from the itemset, adding the minimum confidence threshold (MinConf) as cutoff criteria instead of using the minimum support threshold (MinSupp) as the main criteria for candidate itemset selection , using a text file to store transactions to reduce memory usage [1].…”

“…This abundance of data appears as a consequence of the new technologies that have been developed in the medical field and that allow easy data collection. Obtaining these large databases (big data) has opened up many research opportunities by analyzing the correlations in these large databases [1].…”

R-Diffset vs. IR-Diffset: Comparison Analysis in Dense and Sparse Data

“…The prediction tasks attempt to determine the value of one attribute depending on the value of another. These tasks incorporate techniques like statistics, categorization, regression, and forecasting [19]. Meanwhile, the descriptive tasks are to generate patterns in the database in order to extract the underlying relationships [29].…”