Product Recommendation System using MLP Algorithm

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.

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.

In traditional association rule mining algorithms, if the minimum support is set too high, many valuable rules will be lost. However, if the value is set too low, then numerous trivial rules will be generated. To overcome the difficulty of setting minimum support values, global and local patterns are mined herein. Owing to the temporal factor in association rule mining, an itemset may not occur frequently in the entire dataset (meaning that it is not a global p attern), but it may appear frequently over specific intervals (meaning that it is a local pattern). This paper proposed a temporal ass ociation rule mining algorithm for interval frequent-patterns, called GLFMiner, which automatically and efficiently generates all intervals w ithout prior domain knowledge in an efficient manner. GLFMiner considers not only global frequent-patterns, but also local frequent-patterns. Using the same value of minimum support, it can locate many valuable temporal rules without losing the rules that traditio nal algorithms may find. Experimental results reveal that our novel algorithm mines more temporal frequent-patterns than traditional association rule mining algorithms and is effective in real-world applications such as market basket analysis and intrusion detection systems.

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

To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic Algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding and the fitness function. Through using probability vector possessing learning properties to update the population, the algorithm increases the convergence speed and enhances the searching ability, compared to GA. In the experiment of mining association, rules in blood indices data, PBIL algorithm performs better not only in running time, convergence speed, but also achieve better searching results. Meanwhile, this paper proposed a parallel algorithm for association rule mining based on PBIL and designed a system architecture based on cloud computing for blood indices analysis, providing a good example to apply the new algorithm to cloud computing.

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

In this chapter, we present granule based inter association mining to reduce the complexity of intertransaction association rule mining. To compare with other methods, our method can reduce the width of sliding windows. It uses granules to replace extended item sets. Thus, we do not need to consider too many combinations of extended items. We also propose the concept of precision in order to evaluate the effectiveness of intertransaction association rule mining. The experiments show that the proposed method is promising.

Big data has several applications like page bank algorithm, recommendation system, sentimental analysis, and many more. Recommendation is the key to success for discovering and retrieval of content in this era of huge data [1]. Association Rule Mining (ARM) algorithm is mostly used for recommendation systems. User behaviour is given input to ARM in key/value pair format which can be generated by Map-Reduce function [2]. ARM attempts to find frequent item sets among large datasets and describes the association relationships among different attributes. Market-Basket analysis is the major application of ARM used to predict customer's purchasing behaviour and recommend product to customer [3]. For implementing this recommendation systems there are several programming languages and scala is one among them which is an object oriented and also supports functional programming [4]. For processing large amount of data, functional programming is faster and easier, but in some cases we need to provide security for sensitive data. Also scala runs on Java Virtual Machine (JVM) and also scala can execute Java code since scala code is converted to Java Byte code when compiled. So scala will have a better advantage to implement big data applications.

Data mining examines large pre-existing databases in order to generate new information. There are various tasks included under Data mining and association rule mining is considered as one of the crucial tasks among its. They are in form of if-then kind of statements which help to find relationships among huge data which do not hold relationship with each other within a relational database or any other information repository. As there are many applications like market basket analysis, detection of fraud in web, medical diagnosis, census data, Customer Relationship Management of credit card business which uses association rules so it is possible to improve the process of Decision making. Security is required for individual transaction and for frequent itemsets when the database is partitioned horizontally among multiple sites. In this case, every site is interested in globally supported association rules without revealing its own local information. To fulfill this goal, We use a secure multi-party algorithm based on secure sum technique to simplify the operation of mining association rule when the database is horizontally partitioned among multiple sites. We are using a Frequent-Pattern (FP) growth algorithm to find frequent itemsets and try to reduce total computation time.

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.

Genetic algorithm is an important algorithm of association rule mining. However, there is some issues that genetic algorithm easy to lead prematuring convergence and into the plight of local optimum, or convergence too much time and consume a large amount of time to search. For resolving this issues, the paper improves the algorithm through adopting an adaptive mutation rate and improving the methods of individual choice, and the improved genetic algorithm that applies to the mining association rules. The simulating experiments show that the improved genetic algorithm reduces the cost of computing, and improve the efficiency of association rule mining.

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.

In recent years, the data mining technology has been developed rapidly. New efficient algorithms are emerging. Association data mining plays an important role in data mining, and the frequent item sets are the highest and the most costly. This paper is based on the association rules data mining technology. The advantages and disadvantages of Apriori algorithm and FP-growth algorithm are deeply analyzed in the association rules, and a new algorithm is proposed, finally, the performance of the algorithm is compared with the experimental results. It provides a reference for the extension and improvement of the algorithm of association rule mining.

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.

Frequent itemset mining and association rule mining are fundamental problems in data mining. Despite of the intensive and continuous researches on frequent itemset mining, one essential and not completely solved drawback still remains. The drawback is pattern explosion: a huge number of frequent itemsets, or patterns, will be often derived. The association rule mining takes over the same drawback since each association rule consists of two frequent itemsets. One promising solution for alleviating this essential drawback practically as a post-processing is to rank the patterns and rules from various aspects and to identify which one to be examined first. In this paper, we propose to utilize the technology of representation learning to rank frequent itemsets and association rules. More specifically, we develop several evaluation measures for frequent itemsets and association rules by using the vector representations of items, transactions and frequent patterns which are obtained by existing representation learning techniques. By employing vector representations of three kinds of objects, various rankings reflecting different aspects can expect to be obtained.