EDP-ORD: Efficient distributed/parallel Optimal Rule Discovery

In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here, we present a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery schemes such as nonredundant association rule discovery. We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure and implementation. Optimal rule discovery is an efficient alternative to association rule discovery, especially when the minimum support is low.

MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules

The initial decision after treatment in the hospital emergency room is very important because apart from being an indicator of the quality of care for emergency room practitioners, it is also needed to achieve health goals, namely improving the quality of critical care and preventing death. The initial decision was also for an unplanned ICU transfer. Unplanned ICU transfer is the transfer of patients who originally came from the ER (Emergency Room), then to the Inpatient Room (having been treated for 24-48 hours), then to the ICU. Many studies have been carried out to predict the initial decision of unplanned ICU transfer using univariate analysis, logistic regression analysis, and association rules. The association rule algorithm generates rules between patient diagnosis features that form a decision model for unplanned ICU transfers, so it is essential to get an association rule algorithm that is more efficient in generating rules. In this study, we compare two association rule algorithms to get a more efficient algorithm; then, the rules are used to form a decision model for unplanned ICU transfers. The study results obtained that the Apriori algorithm requires a completion time of 3 ms and the FP-Growth algorithm requires a completion time of 31 ms. Hence, the FP-Growth algorithm is 28 ms more efficient than the Apriori algorithm, while the resulting rule generation is the same number of 67 rules. Only 11 rules meet the minsupp and minconf threshold and include the set of Class Association Rules (CAR), which are used to form a decision model for unplanned ICU transfers with binary integer programming

A provided data from a transactional database, has supported technical development which can automatically find product association or items saved in the database. This finding association rules between saved product in database known as mining association rules. There are so many theories and algorithm developed for conducting mining association rules. One of algorithm developed is Apriori algorithm. This method, has a main goal to find the maximum frequent itemset. Next, this frequent itemset will be generated into associative rules which are not shown before in the database, become valuable information for considering materials in the decision process. Apriori algorithm is a interpretation technique of mining association rules, will be implemented into a web based software. On the software test which use in some different data, it’s concluded that time for mining association rules depends on the presence of every item in every transaction, total of transaction, minimum support and minimum confidence. For smaller value of minimum support and minimum confidence that entered, program will generate more association rules, vice versa.

The approach presented in this paper is to discover association rules based on a user’s query. Of the many issues in rule discovery, relevancy, interestingness, and supportiveness of association rules are considered in this paper. For a given user query, a database can be partitioned into three views: a positively-related-query view, a negatively-related-query view, and an unrelated-query viewWe present a methodology for data mining and rule discovery that incorporates pattern extraction from the three types of query views with pattern spanning to enlarge the scope of a pattern and its derived association rule. The rule discovery process involves several interrelated steps: 1) pattern extraction from both positively- and/or negatively-related query views, 2) pattern association across attributes to enhance the semantics of patterns, while performing 3) pattern spanning within an attribute domain to enhance the supportiveness of the resulting patternThe contributions of the paper includes the specification and development of the data mining method and associated tool that combines the operations of association and spanning on query views to derive semantically interesting patterns. These patterns can then be used in decision making because the patterns were mined from the user’s original hypothesis as expressed by the query

One of the recent research topics in databases is Data Mining, to find, extract and mine the useful information from databases. In case of updating transactions in the database the already discovered knowledge may become invalid. So we need efficient knowledge management techniques for finding the updated knowledge from the database. There have been lot of research in data mining, but Knowledge Management in databases is not studied much. One of the data mining techniques is to find association rules from databases. But most of association rule algorithms find association rules from transactional databases. Our research is a further step of the Tree Based Association Rule Mining (TBAR) algorithm, used in relational databases for finding the association rules .In our approach of updating the already discovered knowledge; the proposed algorithm Association Rule Update (ARU), updates the already discovered association rules found through the TBAR algorithm. Our algorithm will be able to find incremental association rules from relational databases and efficiently manage the previously found knowledge.

The Apriori algorithm's frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

We consider approximate versions of fundamental notions of theories of rough sets and association rules. We analyze the complexity of searching for α-reducts, understood as subsets discerning “α-almost” objects from different decision classes, in decision tables. We present how optimal approximate association rules can be derived from data by using heuristics for searching for minimal α-reducts. NP-hardness of the problem of finding optimal approximate association rules is shown as well. It makes the results enabling the usage of rough sets algorithms to the search of association rules extremely important in view of applications.KeywordsAssociation RuleDecision TableDecision ClassClassi CationConditional AttributeThese 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.

Discovery of association rules has been found useful in many applications. In previous work, all items in a basket database are treated uniformly. We generalize this to the case where items are given weights to reflect their importance to the user. The weights may correspond to special promotions on some products, or the profitability of different items. We can mine the weighted association rules with weights. The downward closure property of the support measure in the unweighted case no longer exists and previous algorithms cannot be applied. In this paper, two new algorithms are introduced to handle this problem. In these algorithms we make use of a metric called the k-support bound in the mining process. Experimental results show the efficiency of the algorithms for large databases.

The main objective of this work was to find the association rules between factors affecting user satisfaction in software project by using an association rule discovery technique. Data from 191 software projects were collected and association rules between 15 Selected factors were discovered by FP–growth algorithm. Primarily, 281 association rules were discovered. Rules that were not directly related to user satisfaction were filtered out by simple criteria. In the end, 11 final association rules that passed those criteria were obtained with an average confidence value of 65.30%. These rules can incorporated in the planning and administration of a software project to gain utmost user satisfaction

The discovery of association rules in data mining is an important issue, the core of which is the frequent pattern mining, Apriori algorithm is classical for the association rule mining, but it should repeatedly scan the database and can produce plenty of candidates. By examples, it is proved that Boolean matrix association rules algorithm can improve the algorithmic efficiency by reducing the times of accessing database and without producing candidates.

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis

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.

In this paper we propose a framework for defining and discovering optimal association rules involving a numerical attribute A in the consequent. The consequent has the form of interval conditions A, A≥ x or A ∈ I where I is an interval or a set of intervals of the form [x_l,x_u. The optimality is with respect to leverage, one well known association rule interest measure. The generated rules are called Maximal Leverage Rules MLR and are generated from Distribution Rules. The principle for finding the MLR is related to the Kolmogorov-Smirnov goodness of fit statistical test. We propose different methods for MLR generation, taking into account leverage optimallity and readability. We theoretically demonstrate the optimality of the main exact methods, and measure the leverage loss of approximate methods. We show empirically that the discovery process is scalable.

Manufacturing activities generate large quantities of data and it is no longer practical to rely on traditional manual methods to analyse these data. Data mining offers tools for efficient knowledge extraction, leading to significant improvement in the decision-making process. Association rule mining is one of the most important data mining techniques and has received considerable attention from researchers and practitioners. It aims to identify interesting relationships among a large set of data items. To date, little work has been done to exploit association rule mining tools in manufacturing. This paper presents the basic concepts of association rule mining and describes a number of its recent applications in manufacturing. The paper also presents a case study concerning the use of association rule mining in a steel bar manufacturing company.