Comparison and improvement of association rule mining algorithm

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.

In order to solve these problems how to easily generate candidate frequent item sets and fast compute support of candidate item sets, an algorithm of association rules mining based on binary has been introduced. However, one presented binary mining algorithm is only suitable for mining some relative short frequent item sets since the way of generating candidate item sets is also similar to Apriori, another is only suitable for mining long frequent item sets, which generates candidate item sets by up-down search strategy. And so aiming to mining general frequent item sets, this paper proposes an algorithm of association rules double search mining based on binary, which is different from tradition association rules mining algorithm based on double search strategy. The algorithm doesn’t use combination of set theory to generate candidate item sets but binary logic operation that is also used to compute support of candidate item sets, which can use character digital to reduce the number of scanned transaction. The algorithm gets rid of shortage about some presented algorithms based on binary. The experiment based on above three algorithms indicates that the efficiency about double search strategy is fast and efficient when mining general frequent item sets which aren’t confined.

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

The traditional particle swarm optimization algorithm is generally only suitable for solving the association rule mining of continuous data, and the improved PSO algorithm needs to be used for the association rule mining of discrete data. Firstly, the FP-growth algorithm is used to construct a fuzzy FP-tree to mine frequent itemsets, and then the PSO algorithm is applied to the mined frequent itemsets. Aiming at the shortcomings of the association rule mining algorithm of PSO, an improved association rule data mining algorithm is proposed. This paper proposes application of particle swarm optimization algorithm in improved association rules data mining method. Experiments show that the particle swarm optimization algorithm proposed in this paper is feasible and efficient in improving association rule data mining.

Data mining has been attracted much attention from practitioners and researchers in recent years. Association rules are one of the most important research areas of data mining. Association Rule Mining (ARM) aims to discovers the relationship between the most frequent itemsets. Many algorithms have been developed for mining static datasets. It is nontrivial to maintain such discovered rules from large datasets, this was the main idea behind Incremental Association Rules Mining (IARM), which recently has received much attention from the Data Mining researcher. In this paper, a survey of Incremental Association Rule Mining (IARM) techniques and algorithms that had been developed are categorized and analyzed.

Parallel extraction of association rules from genomics data

Association rules mining is one of the most important problems in knowledge discovery and data mining. The goal of it is to acquire consumption habits of customers by discovering the relationships between items from a transaction database that has a large number of transactions and items. The most compute intensive process for ARM is to find out the frequent 1-itemsets and 2-itemsets. In this paper, we propose a quantum algorithm for finding out the frequent 1-itemsets and 2-itemsets. In our algorithm, to mine the frequent 1-itemsets efficiently, we use the technique of amplitude amplification. To mine the frequent 2-itemsets efficiently, we propose a new tomography scheme, i.e., pure-state-based quantum state tomography. It is shown that our algorithm is potential to achieve exponential speedup in the number of transactions and polynomial speedup in the number of items over the classical algorithm.

Data mining has a great role for making market revenue by its decisive quality. Frequent Itemsets Mining (FIM) is very focused and emerging field of research of data mining. .FIM is used in association rule mining and other data analytic tasks. Frequent itemsets are patterns those occur very frequently in dataset. This paper provides efficacious and in-depth study of FIM algorithms. A comparative study of existing FIM algorithms and their variations are analyzed to find the limitations of these algorithms. Apriori ,Fp Growth, Eclat are basic methodologies for Frequent itemset mining. But there is issue for efficiency and memory consumption. Initially FIM was started in 1990’s and first algorithm, Apriori has drawback of scanning database multiple time, so complexity and memory consumption was high. Then Fp-Growth algorithm required scanning of dataset twice to form Fp-tree so huge memory consumption was required to hold the fp tree for Frequent Itemset Mining. After it Parallel frequent mining (shared and non-shared memory) overcome the drawback of FP-Growth algorithms by balancing the workload of single node to achieve efficiency. Now in present era of big dataset with voluminous and growing data at every moment, more prominent, efficient and scalable algorithm is required as complexity of these algorithms exponentially increases with the growth of rapid data .So objective of the study is in-depth literature review of Frequent Itemsets Mining algorithms to develop an efficient FIM algorithm.

Association rule 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, it treats each item with the same significance although different item may have different significance; thirdly, only the direct association rules are discovered. Mining fuzzy association rules has been proposed to address the first limitation. In this paper, we put forward an idea for mining indirect weighted association rules to resolve the other two limitations, and a discovery algorithm for mining both direct and indirect weighted fuzzy association rules by integrating these three extensions.

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.

Intelligent tutoring system (ITS) creates a new teaching mode, but most ITS are merely e-learning platforms that provide course study, without considering learning processes of learners, which can't effectively help learners to consolidate and review the unmastered knowledge points. Data mining techniques can extract the potential, valuable pattern or regulation from a great quantity of data. An intelligent tutoring system has been designed based on data mining technology that could return the learners feedback about knowledge points. In order to quickly find all frequent patterns, i.e., knowledge points, an improved algorithm for mining association rules based on FP-growth is presented. Experimental results show that the improved algorithm can provide effective decision support, and help learners to improve their learning efficiency.

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.

A cosmetic cheerful shop located in the Marelan area of Medan city is a shop that sells women's cosmetic equipment. The owner of a cheerful cosmetics shop is very disciplined in recording sales and purchases of goods. Every transaction is always recorded and stored in the Microsoft Excel application. Meanwhile, the Ceria Cosmetics store has been running since 2015 and has recorded lots of transactions, both sales and purchases. From 2015 until now the data has been increasing, and so far this data has only been a record and stored in a folder on the store's computer. From the piles of transaction data at the cheerful cosmetics store, the authors took the idea of research to utilize this pile of data. One of the applications of information technology that can be used to utilize the pile of data is the data mining technique. By using data mining, a pile of data from a cheerful cosmetic store can be used to find association patterns. And the application of data mining with association rules can use the FP-Growth algorithm. The FP-Growth algorithm can be used as an alternative to determine the most frequently occurring data set (frequent itemset) in a data set. Later, the combination patterns obtained in this study can be used to arrange the layout of goods (cosmetics) at the Ceria Cosmetics store in Medan Marelan.

Big data has become one of the key sources for valuable information and as information becomes larger it poses some computational challenge in finding a best possible solution for mining association rules and discovering patterns in data. Meta-heuristic algorithm when applied to mining association rules aims to find best possible rules from data without being stuck in local optimal. Example of meta-heuristics algorithm includes genetic algorithm and particle swarm optimization algorithm. Finding appropriate representation of various types of patterns using rough numerical values attributes is still a challenge because most association rules cannot be applied to numerical data without discretization which may lead to information loss. Mining numeric association rules is a hard optimization problem rather than being a discretization, thus, this paper proposes a new meta-heuristic algorithm which uses wolf search algorithm (WSA) for numeric association rule mining from rough values within tolerable ranges.

Association rule mining (ARM) is well-known issue in data mining. It is a combinatorial optimization problem purpose to extract the correlations between items in sizable data-sets. According to the literature study, bio-inspired prove their efficiency in term of time, memory and quality of generated rules. This paper investigates multi-population cooperative version of bat algorithm for association rule mining (BAT-ARM) named MPB-ARM which is based on bat inspired algorithm. The advantage of bat algorithm is the power combination between population-based algorithm and the local search, however, it more powerful in local search. The main factor to judge optimization algorithms is ensuring the interaction between global diverse exploration and local intensive exploitation. To maintain the diversity of bats, in our proposed approach, we introduce a cooperative master-slave strategy between the subpopulations. The experimental results shows that our proposal outperforms other bio-inspired algorithms already exist and cited in the literature including our previous work BAT-ARM.