An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis

Root Cause Analysis (RCA) is often used in manufacturing analysis to prevent the reoccurrence of undesired events. Association rule mining (ARM) was introduced in RCA to extract frequently occur patterns, interesting correlations, associations or casual structures among items in the database. However, frequent pattern mining (FPM) using Apriori-like algorithms and support-confidence framework suffers from the myth of rare item problem in nature. This has greatly reduced the performance of RCA, especially in manufacturing domain, where existence of imbalanced data is a norm in a production plant. In addition, exponential growth of data causes high computational costs in Apriori-like algorithms. Hence, this research aims to propose a two stage FPM, integrating Principal Component Analysis (PCA) and Weighted Apriori-T (PCA-WAT) algorithm to address these problems. PCA is used to generate item weight by considering maximally distributed covariance to normalise the effect of rare items. Using PCA, significant rare item will have a higher weight while less significant high occurance item will have a lower weight. On the other hand, Apriori-T with indexing enumeration tree is used for low cost FPM. A semiconductor manufacturing case study with Work In Progress data and true alarm data is used to proof the proposed algorithm. The proposed PCA-WAT algorithm is benchmarked with the Apriori and Apriori-T algorithms.Comparison analysis on weighted support has been performed to evaluate the capability of PCA in normalising item’s support value. The experimental results have proven that PCA is able to normalise the item support value and reduce the influence of imbalance data in FPM.Both quality and performance measure are used as performance measurement. The quality measures aim to compare the frequent itemsets and interesting rules generated across different support and confidence thresholds, ranging from 5% to 20%, and 10% to 90% respectively.The rules validation involves a business analyst from the related field. The domain expert has verified that the generated rules are able to explain the contributing factors towards failure analysis. However, significant rare rules are not easily discovered because the normalized weighted support values are generally lower compared to the original suppport values. The performance measures aim to compare the execution time in second (s) and the execution Random Access Memory (RAM) in megabyte (MB). The experiment results proven that the implementation of Apriori-T has lowered the computational cost by at least 90% of computation time and 35.33% of computation RAM as compared to Apriori. The primary contribution of this study is to propose a two-stage FPM to perform RCA in manufacturing domain with the existence of imbalanced dataset. In conclusion, the proposed algorithm is able to overcome the rare item issue by implementing covariance based support value normalization and high computational costs issue by implementing indexing enumeration tree structure.Future work of this study should focus on rule interpretation to generate more human understandable rule by novice in data mining. In addition, suitable support and confidence thresholds are needed after the normalisation process to better discover the significant rare itemset

Predicate based association rules mining with new interestingness measure

Association Rule Mining (ARM) is one of the fundamental components in the field of data mining that discovers frequent itemsets and interesting relationships for predicting the associative and correlative behaviours for new data. However, traditional ARM techniques are based on support-confidence that discovers interesting association rules (ARs) using predefined minimum support (minsupp) and minimum confidence (minconf) threshold. In addition, traditional AR techniques only consider frequent items while ignoring rare ones. Thus, a new parameter-less predicated based ARM technique was proposed to address these limitations, which was enhanced to handle the frequent and rare items at the same time. Furthermore, a new interestingness measure, called g measure, was developed to select only highly interesting rules. In this proposed technique, interesting combinations were firstly selected by considering both the frequent and the rare items from a dataset. They were then mapped to the pseudo implications using predefined logical conditions. Later, inference rules were used to validate the pseudo-implications to discover rules within the set of mapped pseudo-implications. The resultant set of interesting rules was then referred to as the predicate based association rules. Zoo, breast cancer, and car evaluation datasets were used for conducting experiments. The results of the experiments were evaluated by its comparison with various classification techniques, traditional ARM technique and the coherent rule mining technique. The predicate-based rule mining approach gained an accuracy of 93.33%. In addition, the results of the g measure were compared with a state-of-the-art interestingness measure developed for a coherent rule mining technique called the h value. Predicate rules were discovered with an average confidence value of 0.754 for the zoo dataset and 0.949 for the breast cancer dataset, while the average confidence of the predicate rules found from the car evaluation dataset was 0.582. Results of this study showed that a set of interesting and highly reliable rules were discovered, including frequent, rare and negative association rules that have a higher confidence value. This research resulted in designing a methodology in rule mining which does not rely on the minsupp and minconf threshold. Also, a complete set of association rules are discovered by the proposed technique. Finally, the interestingness measure property for the selection of combinations from datasets makes it possible to reduce the exponential searching of the rules