Motivation-based association rule mining

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.

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.

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.

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.

Association rule mining (ARM) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high efficiency and accuracy. Two important processes in mining the association rules for classification are rule generation and rule selection. In this paper, a novel high-level feature detection framework using the ARM technique together with the correlations among the feature-value pairs is proposed. A new association rule mining (ARM) algorithm has been developed, where the N-feature-value pairs are generated using a combined measure based on (1) the existence of the (N-1)-feature-value pairs (where N is larger than 1), (2) the correlation between different N-feature-value pairs and the concept classes through multiple correspondence analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intra-similarity. The final association classification rules are selected by using the calculated harmonic mean of the similarity values. The proposed framework enables the automatic discovery and generation of the N-feature-value pair association rules from the N-feature-value pairs for classification. Experimenting with 15 high-level features (concepts) and benchmark data sets from TRECVID, our proposed framework achieves promising performance and outperforms three other well-known classifiers (decision trees, support vector machine, and neural networks) which are commonly used for performance comparison in the TRECVID community.

In the context of the rapid development of supercomputing, the application of tourism culture communication information recommender algorithm is making continuous progress. The mining algorithm on account of AR must be constantly improved to make the tourism system more convenient and flexible. On account of the AR mining algorithm, the advantage lies in the continuous optimization of AR, the dilation of the corpus of AR, the optimization of the logic of AR, the purpose is to make the tourism culture communication information recommender constantly improve, for tourism information recommendation more intelligent and efficient. This paper studies the tourism culture communication information recommender on account of AR mining algorithm, introduces the concept and principle of AR mining algorithm, and expounds the related content of tourism culture communication information system. This paper tested the actual data of the tourism culture communication information recommender on account of the AR mining algorithm. After repeated tests, it finally came to the conclusion that the simulation rates of tourism planning, payment and reservation, information sharing and travel dream recommendation for customers in the tourism system were 88.12%, 92.03%, 95.02% and 99.10% respectively.

Most association rule mining techniques concentrate on finding frequent rules. However, rare association rules are in some cases more interesting than frequent association rules since rare rules represent unexpected or unknown associations. All current algorithms for rare association rule mining use an Apriori level-wise approach which has computationally expensive candidate generation and pruning steps. We propose RP-Tree, a method for mining a subset of rare association rules using a tree structure, and an information gain component that helps to identify the more interesting association rules. Empirical evaluation using a range of real world datasets shows that RP-Tree itemset and rule generation is more time efficient than modified versions of FP-Growth and ARIMA, and discovers 92-100% of all the interesting rare association rules. Additional evaluation using synthetic datasets also shows that RP-Tree is more efficient, in addtion to showing how the execution time of RP-Tree changes with transaction length and rare-item itemset size.

Parallel extraction of association rules from genomics data

Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.

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

Some problems of mining association rules with linguistic terms are discussed. First, an incremental updating algorithm of association rules with linguistic terms is presented. The collection of frequent linguistic attribute sets and its negative border along with their support count are maintained, which makes scan the entire database once at most in the process of updating association rules. The experiment shows that the updating algorithm can not only update association rules effectively but also avoid the repeated cost. Secondly, the parallel algorithm for mining association rules with linguistic terms is presented. The Boolean parallel mining algorithm is improved to discover frequent linguistic attribute sets, and the association rules with at least confidence are generated on all processors. This parallel mining algorithm has fine scale-up, size-up and speed-up.

The issues in the field of association rules mining with specific items are discussed first. To solve the problems in the ordinary algorithm, we put forward a new but efficient mining algorithm with item-constraints, called EclatII. We then give an analysis on the performance of the algorithm as well as on its strategy. The experimental result shows that the Eclatll algorithm is more robust in items of using "low support'"' and "long pattern" association rules than others.

Knowledge discovery in the form of rules has become a useful and meaningful practice in Data Mining. In the last three decades, Association Rule Mining has been considered one of the primary research activities. Introducing fuzzy mathematical theories provides another dimension to make association rules more user-friendly and expressive. Membership degrees and Linguistic Terms play a significant role in the fuzzification process. The use of Fuzzy set concepts in rule generation, however, needs more attention from the researchers in different application domains. This paper is based on the study and findings of some important Fuzzy Association Rule Mining (FARM) algorithms and their applications. Beginning with the overview of Fuzzy Set theory and FARM algorithms, this study gives an analysis and account of the intricacies of algorithm development and applications-based activities in the last three decades. Out of many algorithms, some important FARM algorithms are undertaken for the study and explained with examples. The examples are prepared from both real and synthetic data. Based on the study, some key features of leading algorithms are observed and highlighted. Different research issues and challenges related to FARM are found out and discussed for further research.

This paper presents an association rule mining system that is capable of handling set-valued attributes. Our previous research has exposed us to a variety of real-world biological datasets that contain attributes whose values are sets of elements, instead of just individual elements. However, very few data mining tools accept datasets that contain these set-valued attributes, and none of them allow the mining of association rules directly from this type of data. We introduce in this paper two algorithms for mining (classification) association rules directly from set-valued data and compare their performance. We have implemented a system based on one of these algorithms and have applied it to a number of biological datasets. We describe here our system and highlight its merits by means of comparing the results achieved with it and the failed attempts to mine association rules from those datasets using standard tools. Our system makes the creation of input files containing set-valued data much easier, and makes the mining of association rules directly from these data possible.