WAR: Weighted Association Rules for Item Intensities

The weighted association rules (WARs) mining are made because importance of the items is different. Negative association rules (NARs) play important roles in decision-making. But the misleading rules occur and some rules are uninteresting when discovering positive and negative weighted association rules (PNWARs) simultaneously. So another parameter is added to eliminate the uninteresting rules. A new model in the paper of extending the support-confidence framework with a sliding interest measure could avoid generating misleading rules. An interest measure was defined and added to the mining algorithm for association rules in the model. The interest measure was set according to the demand of users. The experiment demonstrates that the algorithm discovers interesting weighted negative association rules from large database and deletes the contrary rules.

Mining of association rules for basket databases, has been investigated by [1] [3] [4], [9], [12], etc. Most of these works focus on mining binary association rules, i.e, most of the association rules mining algorithms to discover frequent itemsets do not consider the quantity in which items have been purchased. This paper discusses an efficient method for discovering a weighted association rules from a large volumes of data in a single scan of the database. The data structure used here is called Weighted Tree. We found that this algorithm is more efficient than Cai's Algorithm.

Discovery of association rules has been found useful in many applications. With large database, the process of mining association rules is time consuming. The efficiency becomes crucial factor. Weighted association is more meaningful in some application. This paper implements a fast and stable algorithm to mining weighted association rules base on the open source library Lucene. The methods to create index in Lucene and utilization of the index to find weighted frequent itemsets are introduced. Based on apriori algorithm, a weighted association rule mining algorithm is implemented. All the rules can be recommended to customer by searching Lucene Index. Experiment shows that this method is more efficient than general apriori algorithm.

With the development of mobile communication technology and computer technology, further requirements are put forward to provide decision support for the individual or customers on the move. Association rule mining is a way to solve this problem. Firstly, the association rule mining model in mobile computing is presented and then the concepts of weighted items and weighted association rules are proposed based on association rule mining in mobile computing environment. On this basis, weighted frequent pattern tree is given to mine weighted frequent itemsets in the mobile environment, at the same time a weighted frequent pattern tree construction algorithm and association rule extraction method are given, which provides a variety of auxiliary decision support services for mobile users.

The rapid growth of web services need efficiently discovering the desired web services for the users. Web service interfaces are defined with WSDL that is described by a bag of terms. Many similarity metrics are proposed to solve this problem, it is hardly to resolve the problem that only few pairs of terms between two services have high semantic distance, the semantic distance of other terms between two services are low. Using traditional keyword search metrics may acquire a wrong result that these two web services are similar, in addition, semantics of the web services is hardly to exploit. In this work the authors firstly help the request service to find the services that belong to the same class, and then they use association rule to find terms that are often appear together and find the most similar terms. The authors weaken the weight of the most similar term contained in an association rule and enhance the other terms' weight contained in an association rule to solve the situation above. The experiments show that our approach outperforms some searching methods.

This study aims to introduce a new concept of weighted association rule mining. The purpose is to discover cross section relationship among items and extract the unknown patterns. We proposed two algorithms called HWA (O) and HWA (P) based on the concept that greater the difference among items in an association rule, the higher the weight score is. Hierarchical weights in HWA (O) are assigned according to the hierarchical levels of the items within the itemset and HWA (P) is assigned by a more sophisticated thought proposed. We compared performance of the number of frequent itemsets, number of rules, and content of rules in HWA (O), HWA (P), and Apriori algorithms. As the result, HWA (P) performs better than two other algorithms. At the end of the research, we provide suggestions for retailing managers based on the discovery in the study. To conclude, the algorithm we proposed can efficiently filter out the minor rules and extracts the implicit and unknown patterns. Marketing managers can make decisions more precisely and satisfy customers' needs at the same time.

The association rule mining can be divided into two steps. The first step is to find out all frequent itemsets, whose occurrences are greater than or equal to the user-specified threshold. The second step is to generate reliable association rules based on all frequent itemsets found in the first step. Identifying all frequent itemsets in a large database dominates the overall performance in the association rule mining. In this paper, we propose an efficient hash-based method, HMFS, for discovering the maximal frequent itemsets. The HMFS method combines the advantages of both the DHP (Direct Hashing and Pruning) and the Pincer-Search algorithms. The combination leads to two advantages. First, the HMFS method, in general, can reduce the number of database scans. Second, the HMFS can filter the infrequent candidate itemsets and can use the filtered itemsets to find the maximal frequent itemsets. These two advantages can reduce the overall computing time of finding the maximal frequent itemsets. In addition, the HMFS method also provides an efficient mechanism to construct the maximal frequent candidate itemsets to reduce the search space. We have implemented the HMFS method along with the DHP and the Pincer-Search algorithms on a Pentium III 800 MHz PC. The experimental results show that the HMFS method has better performance than the DHP and the Pincer-Search algorithms for most of test cases. In particular, our method has significant improvement over the DHP and the Pincer-Search algorithms when the size of a database is large and the length of the longest itemset is relatively long.

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.

DEFINITION Let I be a set of binary-valued attributes, called items. A set X ⊆ I is called an itemset. A transaction database D is a multiset of itemsets, where each itemset, called a transaction, has a unique identifier, called a tid. The support of an itemset X in a dataset D, denoted sup(X), is the fraction of transactions in D where X appears as a subset. X is said to be a frequent itemset in D if sup(X) ≥ minsup, where minsup is a user defined minimum support threshold. An (frequent) itemset is called closed if it has no (frequent) superset having the same support. An association rule is an expression A ⇒ B, where A and B are itemsets, and A ∩ B = ∅. The support of the rule is the joint probability of a transaction containing both A and B, given as sup(A ⇒ B) = P (A ∧ B) = sup(A ∪ B). The confidence of a rule is the conditional probability that a transaction contains B, given that it contains A, given as: conf(A ⇒ B) = P (B|A) = P (A∧B) P (A) = sup(A∪B) sup(A) . A rule is frequent if the itemset A∪B is frequent. A rule is confident if conf ≥ minconf , where minconf is a user-specified minimum threshold. The aim of non-redundant association rule mining is to generate a rule basis, a small, non-redundant set of rules, from which all other association rules can be derived.

In order to trace the changes of association rules over an online data stream efficiently, this paper proposes two different methods of generating all association rules directly over the changing set of currently frequent itemsets. While all of the currently frequent itemsets are monitored by the estDec method, all the association rules of every frequent itemset in the prefix tree of the estDec method are generated. For this purpose, a traversal stack is introduced to efficiently enumerate all association rules. These online methods can avoid the drawbacks of the conventional two-step approach. In an on-line environment, a user may be interested in finding those association rules whose antecedents or consequents are fixed to be a specific itemset. Since generating all the association rules may take too long to produce them timely, two additional methods, namely Assoc-X and Assoc-Y, are introduced. Finally, the proposed methods are compared by a series of experiments to identify their various characteristics.

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.

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.

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

In this chapter, we present granule based inter association mining to reduce the complexity of intertransaction association rule mining. To compare with other methods, our method can reduce the width of sliding windows. It uses granules to replace extended item sets. Thus, we do not need to consider too many combinations of extended items. We also propose the concept of precision in order to evaluate the effectiveness of intertransaction association rule mining. The experiments show that the proposed method is promising.

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.