Association Rule Discovery Algorithm Research Articles

Concept Lattice Method for Spatial Association Discovery in the Urban Service Industry

A relative lag in research methods, technical means and research paradigms has restricted the rapid development of geography and urban computing. Hence, there is a certain gap between urban data and industry applications. In this paper, a spatial association discovery framework for the urban service industry based on a concept lattice is proposed. First, location data are used to form the formal context expressed by 0 and 1. Frequent closed itemsets and a concept lattice are computed on the basis of the formal context of the urban service industry. Frequent closed itemsets can filter out redundant information in frequent itemsets, uniquely determine the complete set of all frequent itemsets, and be orders of magnitude smaller than the latter. Second, spatial frequent closed itemsets and association rules discovery algorithms are designed and built based on the formal context. The inputs of the frequent closed itemsets discovery algorithms include the given formal context and frequent threshold value, while the outputs are all frequent closed itemsets and the partial order relationship between them. Newly added attributes create new concepts to guarantee the uniqueness of the new spatial association concept. The inputs of spatial association rules discovery algorithms include frequent closed itemsets and confidence threshold values, and a rule is confident when and only if its confidence degree is not less than the confidence threshold value. Third, the spatial association of the urban service industry in Nanning, China is taken as a case to verify the method. The results are basically consistent with the spatial distribution of the urban service industry in Nanning City. This study enriches the theories and methods of geography as well as urban computing, and these findings can provide guidance for location-based service planning and management of urban services.

Knowledge-Based Error Detection in External Beam Physician Orders Using Association Rules

The physician orders for external beam radiation therapy (EBRT) are associated with top-level treatment decision parameters including prescription dose, number of treatment fractions, treatment modality, treatment positioning, image guidance, etc. Physician order errors manifest as wrong values of individual parameters or logical inconsistencies between multiple parameters, which are difficult to detect even to human experts without going through many data and documentations. The purpose of this work is to investigate an association rule based approach to error detection in physician orders. The goal is to catch those errors earlier so to avoid the costly re-simulation and re-planning. Clinical physician orders for patients who received EBRT treatments from 2008 to 2017 at author’s institution. A total of 3059 individual single-prescription orders for nine disease sites – brain, breast, lung, pelvic, pelvis, prostate, spine, TBI, extremely – were acquired. Each order includes disease attributes and prescription parameters. Seven disease attributes were considered as conditions - site, tumor stage, nodal stage, metastatic stage, intent, laterality and previous treatment. Errors were detected on the four prescription parameters - total dose, fractions, technique and modality. The a priori algorithm was employed to extract frequent item sets from the historical physician orders. The association rules were generated by arranging items in each frequent item set as antecedent items and consequent items. The active association rules were selected according to their support and confident scores. The error detection tool raises an error flag if a new physician order breaks any active association rules. 10 percent of physician orders were randomly chosen and errors (wrong values in prescription parameters) were added manually for testing the performance of the method. A total of 257 active association rules were selected on average for each individual disease sites. The mean values of true positive and false positive rates of error detection were 92.38% and 10.23% respectively for single-prescription cases of nine disease sites. The wrong value of individual physician order parameters and logical inconsistence between physician order parameters could be detected by applying association rules with high positive rate, which could be further improved by optimizing the association rule discovery algorithm. The association rules are human expert understandable and verifiable, and linked directly to historical physician orders. Association rule discovery algorithm can naturally handle physician orders with missing values existing in more than 50% physician orders. The approach supports incorporation into independent error detection tools for assisting manual double-checks on physician orders. The success of the method here also gives promise to further scaling to include patient setup parameters and all treatment sites.

Incremental topological spatial association rule mining and clustering from geographical datasets using probabilistic approach

Due to the dynamic updating of real time spatial databases, the preservation of spatial association rules for dynamic database is a vital issue because the updates may not only invalidate some existing rules but also make other rules relevant. Consequently, the dynamic updating of spatial rules was handled by many researchers through the incremental association rule mining algorithm. Accordingly, in this paper we have developed an incremental topological association rule mining of geographical datasets using probabilistic approach. Initially, the spatial database is read out and it is passed through probability-based incremental association rule discovery algorithm to mine the topological spatial association rules. Once the rules are mined from the spatial database, the assumption here is that the database is dynamically updating for every time interval. In order to handle this dynamic nature, the proposed incremental topological association rule mining process is used in this paper. Here, the candidate topological rule generation is done from the spatial association rules using the topological relations such as, nearby, disjoint, intersects and inside/outside and the topological support is calculated using the proposed probabilistic topological support model. Finally, the spatial clustering is performed based on the mined spatial rules. From the experimentation, we proved that the maximum accuracy reached by the proposed method is 83.14% which is higher than the existing methods, which is defined as the ratio of the occurred rules and total number of topological data objects.

A probability-based incremental association rule discovery algorithm for record insertion and deletion

The maintenance of association rules for dynamic database is an important problem because the updates may not only invalidate some existing rules but also make other rules relevant. This paper is the extension work of probability-based incremental association rule discovery algorithm which can only handle new data insert into a dynamic database. Unlike the previous work, the proposed algorithm can efficiently handle in case of insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains the support counts of frequent itemsets and promising frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Promising frequent itemsets, which are obtained by using the principle of Bernoulli trials, can help to reduce a number of times to rescan the original database. The support counts of new candidate itemsets are approximated by using the principle of maximum possible value. The experimental results show that the execution time of the proposed algorithm is faster than that of Apriori, FUP2, EDUA, and pre-large algorithm.

Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors

Discovering association rules that identify relationships among sets of items is an important problem in data mining. It’s a two steps process, the first step finds all frequent itemsets and the second one constructs association rules from these frequent sets. Finding frequent itemsets is computationally the most expensive step in association rules discovery algorithms. Utilizing parallel architectures has been a viable means for improving FIM algorithms performance. We present two FP-growth implementations that take advantage of multi-core processors and utilize new generation Graphic Processing Units (GPU).

Web Usage Association Rule Mining System

Introduction Web usage log files generated on web servers contain huge amount of information suitable for applying data mining methods to discover potentially useful knowledge (Kosala & Blockeel, 2000; Wang, Li, & Zhang, 2005). Discovering web usage association rules is one of the popular data mining methods that can be applied on the web usage log data. The information contained in association rules can be used to learn about website visitor behaviour patterns, enhance website structure making it more effective for the visitors, or improve web marketing campaigns (Anand, Mulvenna, & Chavielier, 2004; Cooley, Mobasher, & Srivastava, 1997). Originally, association rule mining algorithms were applied to the analysis of transactional databases (Agrawal, Imielinski, & Swami, 1993; Brin, Motwani, Ullman, & Tsur, 1997). When evaluating the association rule interestingness, various measures can be used to help find the rules that give maximally useful information to the user (Geng & Hamilton, 2006; Tan, Kumar, & Srivastava, 2004). Some of the proposed association rule interestingness measures are all-confidence (Omiecinski, 2003), collective strength (Aggarwal & Yu, 1998), conviction and lift (Brin et al., 1997). While association rule finding algorithms are complete in that they find all rules that satisfy defined constraints, they often result in a huge set of rules that is difficult to exploit in order to find those rules that are truly interesting to the user (Liu, Hsu, & Ma, 1999). This problem is aggravated in association rule mining of the web usage log data (Huang, Cercone, & Aijun, 2002). Web usage data is specific and differs from the market basket data in the way that it contains a large number of tightly correlated items (web resources or web pages) due to the link structure of a website. Web pages that are tightly linked together often occur in the same visitor sessions, which is a reason that the generated set of association rules contains a huge number of so called hard association rules, which are not truly interesting to a user (Huang, 2007). There are freely available data mining software systems that can be used for discovering association rules in the web log usage data (Weka 3, n.d.). Weka is a great data mining tool with a wide range of features (Witten, Frank, & Hall, 2011). We used Weka in our previous research (Dimitrijevic & Bosnjak, 2010), but ran into several obstacles. We found that Weka did not contain an integrated set of tools to support all phases of web usage mining. A special software had to be used for web log preprocessing, such as WUM Prep scripts. Further, we had to develop our own special purpose converters of the web log preprocessed data into the Weka's ARFF file format before running web usage association rule discovery algorithm in Weka. Other limitations of the current Weka version are that it offers four association rule interestingess measures without option of combining them. Weka can be extended to include more interestingness measures, but it would involve additional work. There is also no support for automatic pruning of the discovered association rules in Weka using the methods we proposed (this was confirmed to us by the Weka architect in a forum). We concluded that it would give us more flexibility in our research and ability to apply the methods we propose for pruning association rules and for combining various interestingness measures if we used our own independent tool specialized for Web Usage Mining. We present the first version of this tool in this paper. We apply the system for the discovery of the association rules on a real life data set and present the results using various parameter values. We propose a method to alleviate the problem of over-generation of not truly interesting rules in web usage mining by eliminating the rules that contain directly linked pages. …