Visualizing Frequent Itemsets, Association Rules, and Sequential Patterns in Parallel Coordinates

Efficiently mining frequent itemsets and association rules on the encrypted outsourced data remains a great challenge for the time-consuming ciphertext computations. Nowadays, it has been not well addressed for privacy-preserving frequent itemsets and association rule mining schemes with mining efficiency, dataset, and query confidentiality simultaneously. In this paper, we investigate the study of privacy issues on frequent itemset mining and association rule mining on outsourced data in a two-cloud model, where the data are encrypted and outsourced by multiple owners holding different public keys. We develop several secure computation protocols based on additively homomorphic cryptosystem and additive secret sharing, which enable the clouds could securely mine the frequent itemsets and association rules. Furthermore, we also design two kinds of frequent itemset and association rule query service models, i.e., service customers query the cloud-mined results, and service customers query with their own decided threshold. The proposed scheme not only supports the mining process on the data encrypted by multiple public keys without compromising the security of the datasets, query data and query results, but also offline users. In addition, the experimental results show that our query scheme is much more efficient than the state-of-the-art work.

Frequent itemset mining and association rule mining are fundamental problems in data mining. Despite of the intensive and continuous researches on frequent itemset mining, one essential and not completely solved drawback still remains. The drawback is pattern explosion: a huge number of frequent itemsets, or patterns, will be often derived. The association rule mining takes over the same drawback since each association rule consists of two frequent itemsets. One promising solution for alleviating this essential drawback practically as a post-processing is to rank the patterns and rules from various aspects and to identify which one to be examined first. In this paper, we propose to utilize the technology of representation learning to rank frequent itemsets and association rules. More specifically, we develop several evaluation measures for frequent itemsets and association rules by using the vector representations of items, transactions and frequent patterns which are obtained by existing representation learning techniques. By employing vector representations of three kinds of objects, various rankings reflecting different aspects can expect to be obtained.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism

This paper shows a mathematical foundation for almost important features in the problem of discovering knowledge by association rules. The class of frequent itemsets and the association rule set are partitioned into disjoint classes by two equivalence relations based on closures. Thanks to these partitions, efficient parallel algorithms for mining frequent itemsets and association rules can be obtained. Practically, one can mine frequent itemsets as well as association rules just in the classes that users take care of. Then, we obtain structures of each rule class using corresponding order relations. For a given relation, each rule class splits into two subsets of basic and consequence. The basic one contains minimal rules and the consequence one includes in the rules that can be deducted from those minimal rules. In the rest, we consider association rule mining based on order relation min. The explicit form of minimal rules according to that relation is shown. Due to unique representations of frequent itemsets through their generators and corresponding eliminable itemsets, operators for deducting all remaining rules are also suggested. Experimental results show that mining association rules based on relation min is better than the ones based on relations of minmin and minMax in terms of reduction in mining times as well as number of basic rules.

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.

The tasks of extracting (top-K) Frequent Itemsets (FIs) and Association Rules (ARs) are fundamental primitives in data mining and database applications. Exact algorithms for these problems exist and are widely used, but their running time is hindered by the need of scanning the entire dataset, possibly multiple times. High-quality approximations of FIs and ARs are sufficient for most practical uses. Sampling techniques can be used for fast discovery of approximate solutions, but works exploring this technique did not provide satisfactory performance guarantees on the quality of the approximation due to the difficulty of bounding the probability of under- or oversampling any one of an unknown number of frequent itemsets. We circumvent this issue by applying the statistical concept ofVapnik-Chervonenkis (VC) dimensionto develop a novel technique for providing tight bounds on the sample size that guarantees approximation of the (top-K) FIs and ARs within user-specified parameters. The resulting sample size is linearly dependent on the VC-dimension of a range space associated with the dataset. We analyze the VC-dimension of this range space and show that it is upper bounded by an easy-to-compute characteristic quantity of the dataset, thed-index, namely, the maximum integerdsuch that the dataset contains at leastdtransactions of length at leastdsuch that no one of them is a superset of or equal to another. We show that this bound is tight for a large class of datasets. The resulting sample size is a significant improvement over previous known results. We present an extensive experimental evaluation of our technique on real and artificial datasets, demonstrating the practicality of our methods, and showing that they achieve even higher quality approximations than what is guaranteed by the analysis.

This paper proposes an algorithm of mining spatial topology association rules with constraint condition based on Apriori, which is used to mining spatial multilayer transverse association rules with constraint condition from large spatial database. This algorithm generates candidate frequent topological item sets via up search strategy similar to Apriori, which is suitable for mining short spatial topological frequent item sets with constraint condition. This algorithm compresses storage structure of spatial topological relation to create an integer. Via this method, firstly, the algorithm may efficiently reduce some storage space of mining database. Secondly, the algorithm is effortless to distinguish topological relation of two spatial objects, namely, it may fast compute support of candidate item sets. Finally, the algorithm is fast to connect (k+1)-candidate item sets of k-frequent item set by up search strategy. The result of experiment indicates that the algorithm of mining spatial topology association rules with constraint condition based on Apriori is able to extract spatial multilayer transverse association rules with constraint condition from spatial database via efficient data store, and it is very efficient to extract short frequent topology association rules with constraint condition.

Stored data in database can hide some knowledge, which is interesting, useful to hidden knowledge discover. In this context, an algorithms number a frequent itemsets and association rules extraction were presented. Special feature of these algorithms is to generation a large number of rules, making their exploitation a difficult task. In this paper we will introduce a new algorithm for association rules extraction. Proposed solution is based on two points, namely: frequent itemset extraction, and from these, it extracts association rules.

In order to generate synthetic basket datasets for better benchmark testing, it is important to integrate characteristics from real-life databases into the synthetic basket datasets. The characteristics that could be used for this purpose include the frequent itemsets and association rules. The problem of generating synthetic basket datasets from frequent itemsets is generally referred to as inverse frequent itemset mining. In this paper, we show that the problem of approximate inverse frequent itemset mining is NP-complete. Then we propose and analyze an approximate algorithm for approximate inverse frequent itemset mining, and discuss privacy issues related to the synthetic basket dataset. In particular, we propose an approximate algorithm to determine the privacy leakage in a synthetic basket dataset.

The extraction of frequent itemsets and association rules is a fundamental challenge in data mining and holds significant importance within the field. Mining techniques utilising Linear Prefix (LP) growth association rules employ a bottom-up methodology that necessitates a conditional pattern base and a conditional LP-tree for the extraction of frequent itemsets. This research proposes a Linear Prefix tree utilising a Top-Down Approach with Clustering (CLTDLP) method, to address the limitations of the current LP-growth algorithm. The suggested CLTD-LP algorithm employs a top-down methodology; it generates a subheader table to effectively mine common items, rendering the CLTD-LP algorithm more advantageous as it does not create a conditional pattern base or LP-tree. The proposed methodology enhances the algorithm's efficiency regarding execution time and memory utilisation. Overall, across three benchmark datasets, the proposed CLTD-LP algorithm consistently achieves better average reduction in terms of runtime and memory than the existing LP-growth, Ordered Frequent Itemsets Matrix (OFIM) and Simple and Scalable Frequent Itemset Mining(SSFIM) algorithms.

Association rules have received lots of attention in data mining due to their many applications in marketing, advertising, inventory control, and many other areas.KeywordsAssociation RuleTransaction DataCandidate ObjectApriori AlgorithmShopping CartThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the popularization of e-commerce, understanding and predicting consumer behavior are crucial. Therefore, we developed a consumer behavior segmentation method based on the Apriori algorithm to optimize marketing campaigns by mining consumer purchase patterns. By analyzing a large amount of transaction data, frequent item sets and association rules were identified, and consumers were classified into different segments. These segments help ecommerce enterprises locate their target customer groups more precisely, develop personalized marketing strategies, and improve customer satisfaction and loyalty. The developed model significantly improves the conversion rate and the return on investment of marketing campaigns.

Interactive learning environment is the key support for education decision making, the corresponding analytics and methodology are the important part of educational technology research and development. As an important part and the research challenge, learning behaviors are uncertain and produce complex data relationships, which makes the learning analysis process more difficult. This chapter studies the feasibility of Eclat framework applying in educational decision making and get the corresponding the data analysis results. We take probabilistic frequent itemsets and association rules as research objectives, extract and standardize multiple data subsets; Based on Eclat framework, using data vertical format, we design and improve the models and algorithms in the process of data management and processing. The results show that the improved models and algorithms are effective and feasible. On the premise of ensuring robustness and stability, the mining quality of probabilistic frequent itemsets and association rules is guaranteed, which is conducive to the construction of key execution topology of learning behaviors, and improves the accuracy and reliability of data association analysis and decision prediction. The whole analysis methods and demonstration processes can provide references for the study of interactive learning environment, as well as decision suggestions and predictive feedback.

Generalized association rule mining using an efficient data structure

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