Association Rules In Databases Research Articles

The Effect of Social Media on Student Learning Motivation Using the Apriori Method

The success of student learning can be determined by their motivation. Students who have high learning motivation tend to have high achievement as well, otherwise their learning motivation is low, their learning achievement will also be low. student learning motivation in the subject is very low. Some students prefer to play social media rather than pay attention to the material explained by the teacher during class hours. Therefore, this study aims to explore the influence of social media on students' learning motivation. This research uses data mining method with Apriori algorithm to identify patterns related to social media usage and students' learning motivation. The Apriori algorithm is one of many algorithms in data mining that is used for frequent itemsets and association rules in databases on transactional data that are generated by identifying each item that exists, and combining larger sets of items provided that the items appear frequently enough in the database. Based on the research that has been done, the author can draw the conclusion that using the Rapid Miner 7.1 application tools in applying the apriori algorithm produces the same rules as manual calculations using 300 data on the learning motivation of Abdi Negara Binjai SMKS students and the system can generate association rules using 300 student learning motivation data with a minimum support of 12% and a minimum confidence of 75% and produce 5 association rules 3 itemsets to determine the learning motivation of Abdi Negara Binjai SMKS students. One of the rules that has the highest confidence value is, if YT and J2 then M1. Which means that every student who uses YOUTUBE Social Media with a length of use is 3-4 HOURS then INCREASES STUDY MOTIVATION. Then the less the ɸ (frequent) value is set, the more data that can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.

Storage and query over encrypted sensitive association rules in database

Storage and query over encrypted sensitive association rules in database

Storage and Query Over Encrypted Sensitive Association Rules in Database

In this modern era of increasing need of data sharing along with data security, processing over encrypted data is highly desirable. Data encryption is the most common technique used for maintaining the data privacy. However, when processing is required to be done on this encrypted data it becomes a critical task. In most of the existing theories data need to be decrypted before it is being processed. Decryption before processing leads to serious threat to data. To avoid this, a new mechanism has been proposed and implemented which provides strong data protection. This mechanism helps to query over encrypted data without decrypting it. Experimental results show that the new technique outperforms the existing one in terms of query performance, computation time and storage memory.

ETARM: an efficient top-k association rule mining algorithm

Mining association rules plays an important role in data mining and knowledge discovery since it can reveal strong associations between items in databases. Nevertheless, an important problem with traditional association rule mining methods is that they can generate a huge amount of association rules depending on how parameters are set. However, users are often only interested in finding the strongest rules, and do not want to go through a large amount of rules or wait for these rules to be generated. To address those needs, algorithms have been proposed to mine the top-k association rules in databases, where users can directly set a parameter k to obtain the k most frequent rules. However, a major issue with these techniques is that they remain very costly in terms of execution time and memory. To address this issue, this paper presents a novel algorithm named ETARM (Efficient Top-k Association Rule Miner) to efficiently find the complete set of top-k association rules. The proposed algorithm integrates two novel candidate pruning properties to more effectively reduce the search space. These properties are applied during the candidate selection process to identify items that should not be used to expand a rule based on its confidence, to reduce the number of candidates. An extensive experimental evaluation on six standard benchmark datasets show that the proposed approach outperforms the state-of-the-art TopKRules algorithm both in terms of runtime and memory usage.

IJARCCE - Computer and Communication Engineering

With data mining techniques one can easily disclose other's sensitive information or knowledge. So, preserving privacy for sensitive knowledge has become an important aspect. Privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for sensitive knowledge from disclosure. DSRRC (Decrease Support of R.H.S. item of Rule Clusters) algorithm is used to preserve privacy for sensitive association rules in database. This algorithm clusters the sensitive association rules based on certain criteria by modifying fewer transactions and hides many rules at a time. Moreover it provides privacy for sensitive rules at certain level while ensuring data quality.

An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization

Association rules are one of the most frequently used tools for finding relationships between different attributes in a database. There are various techniques for obtaining these rules, the most common of which are those which give categorical association rules. However, when we need to relate attributes which are numeric and discrete, we turn to methods which generate quantitative association rules, a far less studied method than the above. In addition, when the database is extremely large, many of these tools cannot be used. In this paper, we present an evolutionary tool for finding association rules in databases (both small and large) comprising quantitative and categorical attributes without the need for an a priori discretization of the domain of the numeric attributes. Finally, we evaluate the tool using both real and synthetic databases.

Association Rule Mining for Multiple Tables With Fuzzy Taxonomic Structures

Most of the existing data mining algorithms handle databases consisting of single table to find association rules an large databases. Few algorithms work on multiple tables having fuzzy data with taxonomic structures. This paper proposes 'Multi level Fuzzy rules for ER Models' algorithm. The study focuses on the issue of mining association rules in databases having multiple levels containing fuzzy data with taxonomy and tables to be designed using Entity-Relationship (ER) Models. The study aims to incorporate the previous developed algorithms Extended Apriori and Apriori star to a new algorithm. The study will help in standardizing algorithms for finding appropriate results from database tables containing data with fuzzy taxonomic structures.

Efficient mining of both positive and negative association rules

This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬ B , ¬ A ⇒ B , and ¬ A ⇒ ¬ B , which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.

A fuzzy approach for mining quantitative association rules

During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in databases with binary attributes. In this paper, we deal with the problem of mining association rules in databases containing both quantitative and categorical attributes. An example of such an association might be ``10% of married people between age 50 and 70 have at least 2 cars''''. We introduce a new definition of quantitative association rules based on fuzzy set theory. Using the fuzzy set concept, the discovered rules are more understandable to a human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries. The above example could be rephrased eg. 10% of married old people have several cars. In this paper we present a new algorithm for mining fuzzy quantitative association rules. The algorithm uses new definitions for interesting mea- sures. Experimental results show the efficiency of the algorithm for large databases.

Window-accumulated subsequence matching problem is linear

Given two strings, text t of length n, and pattern p = p 1…p k of length k, and given a natural number w, the subsequence matching problem consists in finding the number of size w windows of text t which contain pattern p as a subsequence, i.e. the letters p 1,…,p k occur in the window, in the same order as in p, but not necessarily consecutively (they may be interleaved with other letters). Subsequence matching is used for finding frequent patterns and association rules in databases. We generalize the Knuth–Morris–Pratt (KMP) pattern matching algorithm; we define a non-conventional kind of RAM, the MP-RAMs which model more closely the microprocessor operations; we design an O(n) on-line algorithm for solving the subsequence matching problem on MP-RAMs.

A lattice-based approach for I/O efficient association rule mining

Most algorithms for association rule mining are variants of the basic Apriori algorithm (Agarwal and Srikant, Fast algorithms for mining association rules in databases, in: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), Santiago, Chile, 1994, pp. 487–499). One characteristic of these Apriori-based algorithms is that candidate itemsets are generated in rounds, with the size of the itemsets incremented by one per round. The number of database scans required by Apriori-based algorithms thus depends on the size of the biggest frequent itemsets. In this paper, we devise a more general candidate set generation algorithm, LGen, which generates candidate itemsets of multiple sizes during each database scan. We present an algorithm FindLarge which uses LGen to find frequent itemsets. We show that, given a reasonable set of suggested frequent itemsets, FindLarge can significantly reduce the number of I/O passes required. In the best cases, only two passes are sufficient to discover all the frequent itemsets irrespective of the size of the biggest ones.Two I/O-saving algorithms, namely DIC and Pincher-Search, are compared with FindLarge in a series of experiments. We discuss the conditions under which FindLarge significantly outperforms the others in terms of I/O efficiency.

Mining weighted association rules

Association rules are useful for determining correlations between items and have applications in marketing, financial and retail sectors. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms treat each item as uniformity. However, in real applications, the user may have more interest in the rules that contain those fashionable items that occur frequently. Usually too many outdated items exist in databases, but they seldom occur recently. Those outdated items hamper us to find the interesting rules efficiently and effectively. Another case is the user sometimes may want to mine the association rules with more emphasis on some items. To solve these problems, in this paper, we propose the vertical and mixed weighted association rules. We can divide the database into several time intervals, and assign a weight for each interval. Furthermore, we also assign a weight for each item to identify the important items. We present an algorithm MWAR (Mixed Weighted Association Rules) to handle the problem of mining mixed weighted association rules. The experiments show that the rules from our methods have much better predictive ability on future data. We also demonstrate the efficiency of our methods on real data and synthetic datasets.

Mining fuzzy association rules in databases

Data mining is the discovery of previously unknown, potentially useful and hidden knowledge in databases. In this paper, we concentrate on the discovery of association rules. Many algorithms have been proposed to find association rules in databases with binary attributes. We introduce the fuzzy association rules of the form, 'If X is A then Y is B ', to deal with quantitative attributes. X, Y are set of attributes and A, B are fuzzy sets which describe X and Y respectively. Using the fuzzy set concept, the discovered rules are more understandable to human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries.