An effective hash-based algorithm for mining association rules

One of the most important data mining problems is mining association rules. In this paper we consider discovering association rules from large transaction databases. The problem of discovering association rules can be decomposed into two sub-problems: find large itemsets and generate association rules from large itemsets. The second sub-problem is easier one and the complexity of discovering association rules is determined by complexity of discovering large itemsets. In this paper, we suggest Apriori-based algorithm for discovering large itemsets. Actually, we suggest a new procedure for large itemsets generation which is more efficient than the appropriate procedure of the original Apriori algorithm. For its implementation, we suggest a modified sort-merge-join algorithm, which is more efficient than nested-loop-join algorithm, which is suggested in the original Apriori algorithm. Besides, we propose a way in which Apriori Multiple finishes in just two iterations.

Multi-dimensional semantic clustering of large databases for association rule mining

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items bought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In addition, we extend our revised algorithm to maintain discovered association rules when incremental or decremental updates are made to the databases. Experimental results show the efficiency of our algorithms. The revised algorithm significantly improves over the original one on mining association rules. The algorithms for maintaining association rules are more efficient than re-running the mining algorithms for the whole updated database and outperform previously proposed algorithms that need multiple passes over the database.

Some problems of mining association rules with linguistic terms are discussed. First, an incremental updating algorithm of association rules with linguistic terms is presented. The collection of frequent linguistic attribute sets and its negative border along with their support count are maintained, which makes scan the entire database once at most in the process of updating association rules. The experiment shows that the updating algorithm can not only update association rules effectively but also avoid the repeated cost. Secondly, the parallel algorithm for mining association rules with linguistic terms is presented. The Boolean parallel mining algorithm is improved to discover frequent linguistic attribute sets, and the association rules with at least confidence are generated on all processors. This parallel mining algorithm has fine scale-up, size-up and speed-up.

To reduce invalid rules in the mining of association rules, we have analyzed the reasons and presented a relative confidence in the judgment criteria. Based on the value of relative confidence, we classify strong association rules into positive, invalid and negative association rules. We offer an algorithm of mining association rules with new judgment criterion and make tests with Visual FoxPro. The tests indicate that the new method stated in this paper can obviously reduce invalid association rules.

As an efficient data analysis tool, data mining can discover the potential association and regularity of massive data, and it has been widely used and played an important role in business decision, medical research and so on. However, the data mining technology is also a double-edged sword, in bringing convenience at the same time, will also cause the user’s privacy leak problem. In order to solve the problem, the symmetric searchable encryption technology is introduced into the association rule mining system to protect the privacy, and privacy preserving mining system of s (PP-MSAR) in OpenStack-based Cloud environment is designed. In order to solve the problem that the existing data mining algorithms can’t deal with large-scale data, this paper uses the computational power of Hadoop platform and add the global pruning technique to the existing algorithm based on MapReduce association rules, so that the counting of frequent item sets get reduced. At the same time, this paper add frequent matrix storage method into the distributed association rules algorithm and realize he algorithm of mining association rules for frequent matrix storage based on MapReduce. In addition, the introduction of symmetric searchable encryption technology to support the cloud server-side ciphertext retrieval, on the one hand to ensure that users stored in the database information will not be leaked to the outside for others, on the other hand also to ensure that the user data for the system staff confidential. Finally, we test the system, and the results show that the system can carry out association rules mining under the premise of protecting user privacy, and provide the correlation degree between data, which has certain practical significance and application value.

Association rules mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining association rules are built on the binary attributes databases, which has three limitations. Firstly, it can not concern quantitative attributes; secondly, only the positive association rules are discovered; thirdly, it treat each item with the same frequency although different item may have different frequency. In this paper, we put forward a discovery algorithm for mining positive and negative fuzzy association rules to resolve these three limitations.

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

Discovery of association rules is one of the most important tasks in data mining. Many efficient algorithms have been proposed in the literature. In this paper, a method of dataset-partitioning using conceptual hierarchy and a variable precision rough set model is presented. An algorithm for mining association rules using this technique is designed, and an asynchronous algorithm is proposed, too. The efficiency of the algorithm and the factors that affect the efficiency of the algorithm are analyzed by mining association rules in a dataset artificially generated. The result of an experiment proves the efficiency of the algorithm.

The mining of association rules is one of the database mining techniques used to extract useful information from large quantities of data. Finding association rules, however, requires that the transaction database be scanned repeatedly, and we need to handle very large amounts of transaction data. This requires an incredibly large amount of computation time. There have therefore been many attempts to speed-up database mining by using parallel computers. Recent improvements in the performance of PCs and workstations (WSs), and the recent dissemination of network technology have made parallel processing distributed within computer clusters an attractive alternative to parallel computers. In this paper we describe two new algorithms effective for parallel processing distributed in a WS cluster environment. One is an algorithm in which the size of data transmitted between WSs is smaller than that of the former algorithm. The other is an algorithm that reduces the amount of scan processing at each node by dividing data and that uses shift operations for data communication. We have implemented these algorithms on a WS cluster and have evaluated their performance.

Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.

Coronary Heart Disease (CHD) is a ordinary cardiovascular disease which does gravely harm to humans' health. It belongs to the category of chest stuffiness and pains on usual Indian Medicine. Many more researcher work on the association improvement is a very important research field in data mining. The mining association rule recurrently has been adopting numerous replica support, confidence, interestingness. But this model can't accurate determine the correlative degree between the precursor and the major of the rule by allocation. So we proposed a new mining algorithm of association rules. Proposed a new system based SVM,fuzzy Discriminant support. Our propose Coronary Heart Disease prediction system quantitatively estimate the correlation of rules and reduce many rules that have low support or have no correlation or have negative correlation. In our work we conduct experiments on large real time rural and urban data set and to predict the diseases like Medication in Coronary Heart Disease.

Recently Negative Association Rule Mining (NARM) has become a focus in the field of spatial data mining. Negative association rules are useful in data analysis to identify objects that conflict with each other or that complement each other. Much effort has been devoted for developing algorithms for efficiently discovering relation between objects in space. All the traditional association rule mining algorithms were developed to find positive associations between objects. By positive correlation we refer to associations between frequently occurring objects in space such as a city is always located near a river and so on. Recently the problem of identifying negative associations (or “dissociations”) that is absence of objects has been explored and considered relevant. This paper presents an improved design approach for mining both positive and negative association rules in spatial databases. This approach extends traditional association rules to include negative association rules using a minimum support count. Experimental results show that this approach is efficient on simple and sparse datasets when minimum support is high to some degree, and it overcomes some limitations of the previous mining methods. The proposed form will extend related applications of negative association rules to a greater extent.

Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Transactions with quantitative values and items with hierarchical relationships are, however, commonly seen in real-world applications. This paper proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level taxonomy to find fuzzy association rules from transaction data sets. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexity.

A novel software defect prediction approach via weighted classification based on association rule mining