Association Rule Mining

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.

Association rules are one of the data and web mining techniques which aim to discover the frequent patterns among itemsets in a transactional database. Frequent patterns and correlation between itemsets in datasets and databases are extracted by these interesting rules. The association rules are positive or negative, and each has its own specific characteristics and definitions. The mentioned algorithms of the discovery of association rules are always facing challenges, including the extraction of only positive rules, while negative rules in databases are also important for a manager’s decision making. Also, the threshold level for support and confidence criteria is always manual with trial and error by the user and the proper place or the characteristics of datasets is not clear for these rules. This research analyses the behavior of the negative association rules based on trial and error. After analyzing the available algorithms, the most efficient algorithm is implemented and then the negative rules are extracted. This test repeats on several standard datasets to evaluate the behavior of the negative rules. The analyses of the achieved outputs reveal that some of the interesting patterns are detected by the negative rules, while the positive rules could not detect such helpful rules. This study emphasizes that extracting only positive rules for covering association rules is not enough.