Clustering Association Rules

1999

Association rule mining is one of the most important procedures in data mining. In industry applications, often more than 10,000 rules are discovered. To allow manual insepection and support knowledge discovery the number of rules has to be reduced significantly by techniques such as pruning or grouping. In this paper, we present a new normalized distance metric to group association rules. Based on these distances, an agglomerative clustering algoritm is used to cluster the rules. Also the rules are embedded in a vector space by multi-dimensional scaling and clustered using a self organizing feature map. The results are combined for visualization. We compare various distance measures and illustrate subjective and objective cluster purity on results obtained from real data-sets. § © . These direct features are very limited in capturing the interaction of rules on the data and characterize only a single rule.

International Journal of Computer Science and Information Technology

The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.

Data mining play an important role in extracting information or patterns from large database such as datawarehouse and XML repository. In this research we process a technique Clusters are used in fuzzy association rule. It was used to find all the rules that satisfy the minimum support and minimum confidence constraints. In this proposed work new patterns match technique to group association rules, based on the similar attributes, pattern matching clustering algorithm is used to cluster the rules. This research work is used to combine more number of rules with a conditional value. Based on the conditional value, the result will be declared whether the rules or cluster or not.

International Conference on Computer …, 2003

The fundamental concept of a partition links almost all knowledge discovery and data mining techniques. Such fundamental and unifying concepts are very important since there is such a wide variety of problem domains covered under the general headings of knowledge discovery and data mining. For instance, a data store that tries to analyze shopping behavior would not benefit much from a machine learning algorithm that allows prediction of one quantity as a function of some number of other variables. Yet, such an algorithm may be precisely the right tool for an agricultural producer who wants to predict yield from the nitrogen and moisture values in his field. We will show that both problems and their solutions, can be described in the framework of partitions and generalized database operations.

International Journal of Computer Theory and Engineering, 2012

In this paper the problem of discovering association rules among items in extremely large databases has been considered. A novel mining algorithm named Improved Cluster Based Association Rules (ICBAR) has been proposed which can explore efficiently the large itemsets. Achieving this and initializing the cluster table (where transaction records with length k are placed in kth cluster table), database will be once scanned. Simultaneously an array with appropriate size for each itemset (named itemset array (IA)) will be created. Here kth element in the array of each itemset indicates number of transaction records in kth cluster table which have that itemset. Presented method not only prunes considerable amounts of data by comparing with the partial cluster tables but also reduces the number of large candidate itemset that must be checked in each cluster through itemset arrays. Performance and efficiency of proposed method has been compared with CBAR and Apriori algorithms. Experiments illustrate that ICBAR will do better than both of them.

IAEME PUBLICATION, 2013

Data mining is involved with the use of advanced data analysis tools to find out new, suitable patterns and project the relationship among the patterns which were not known prior. In data mining, association rule learning is a more suitable method for ascertaining new relations between variables in large databases. The objective the technique focuses on the formulation of association rules. The discovery of association relationships among large amount of transactions as well as data may be vital for making multi decisions. Numerous algorithms are available to discover association rules. Usually quite few algorithms depend on the use of minimum support whereas other algorithms are inclined to highly interrelated items. In this paper it is intended to describe the association rule algorithms and a comparison of two algorithms representative of these approaches, e.g. support and confidence based approaches.

Engineering, Technology & Applied Science Research, 2023

Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.

We i n troduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be 10 of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by ne-partitioning the values of the attribute and then combining adjacent partitions as necessary. W e i n troduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a greater-than-expected-value" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, w e describe the results of using this approach o n a real-life dataset.

CIIT- Data Mining and Knowledge Discovery, 2018

Data mining practices expert procedures and methods to identify the tendencies and profiles concealed in data. Mining is an iterative process in a sequence. Different sources of data are stored in different databases. The mining depends on databases. This research is for various association rule mining applications of different databases. There are different databases in practice like large database, distributed database, medical database, relational database, spatial database. The process of mining these databases are carried out by different data mining techniques. For making decisions, association rule is most essential. They are associated with association rule mining techniques.

Journal of Advances in Information Technology, 2012

In proposed approach, we introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. We have proposed an algorithm for Discovery of Scalable Association Rules from large set of multidimensional quantitative datasets using k-means clustering method based on the range of the attributes in the rules and Equidepth partitioning using scale k-means for obtaining better association rules with high support and confidence. The discretization process is used to create intervals of values for every one of the attributes in order to generate the association rules. The result of the proposed algorithm discover association rules with high confidence and support in representing relevant patterns between project attributes using the scalable k-means .The experimental studies of proposed algorithm have been done and obtain results are quite encouraging.