Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

Exploring ant-based algorithms for gene expression data analysis

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.

In the last years, there has been a large growth in the research of computational techniques inspired in nature. This area, named Bioinspired Computing, has provided biologically motivated solutions for several real world problems. Among Bioinspired Computing techniques, one can mention Artificial Neural Networks (ANN), Evolutionary Algorithms (EA), Artificial Immune Systems (AIS) and Ant Colony Optimization (ACO). ACO is a meta-heuristic based on the structure and behavior of ant colonies. It has been successfully applied to several optimization problems. Several real world optimization problems may change the configuration of its search space with time. These problems are known as dynamic optimization problems. This chapter presents the main concepts of ACO and show how it can be applied to solve real optimization problems on dynamic environments. As a case study, it will be illustrated how ACO can be applied to process scheduling problems. The chapter starts describing how real ants forage for food, environmental modifications related to this activity (for example, the blockage of a path) and the effects of these modifications in the colony. After, a brief review of previous works on Ant Colony Optimization is presented. For computer simulations, the activities of an ant colony may be modeled as a graph. After modeling the problem of foraging for food (as a graph), the goal (finding the shortest path at a given moment) is defined and the mechanism to solve the problem is presented. The need to model an on-line problem as a graph, the goal of finding the shortest path and the mechanisms adopted for solving the problem (an adapted version of the AntSystem) are detailed. Before ACO is applied to the process scheduling problem, the problem is analyzed and modeled using a graph representation. Next, simulation results obtained in a set of experiments are presented, which are validated by results obtained in a real implementation. Important issues related with the use of ACO for process scheduling, like parameter adjustments, are discussed. In the conclusion of this chapter, we point out a few future directions for ACO researches. The use of computational techniques inspired in nature has become very frequent in the last years. This area, named Bioinspired Computing, provides efficient biologically motivated

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.

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.

Ant Colony Optimization (ACO) is a problem-solving technique inspired by the behavior of real-world ant colony. ACO-based routing also has high potential on balancing the traffic load in the domain of Network-on-Chip (NoC), where the performance is generally dominated by traffic distribution and routing. Since the pheromone in ACO provides both spatial and temporal network information, we find ACO-based routing suitable for reducing the probability of deadlock and its penalty. With the three schemes inspired by the behavior of ants and named as ACO-based Deadlock-Aware Routing (ACO-DAR), our simulation shows that the occurrence of deadlock can be greatly suppressed and the network performance also improves as a consequence. Moreover, ACO-DAR makes use of the existing hardware of the original ACO-based routing, so the area overhead is minor and ACO-DAR is thus cost-effective.

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.

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.

Parallel extraction of association rules from genomics data

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.

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.

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.

Existing algorithms for support-based association rule mining (ARM) can not discover the itemsets which are scarce but have high utility values, while utility-based association rule mining (UBARM) can not discover the itemsets whose utility values are not high but the product of the support and utility of the same itemset (defined as motivation) is very large. This paper proposes motivation-based association rule and a down-top algorithm called HM-miner to discover all high motivation item-sets efficiently. By integrating the advantages of support and utility, the new measure, i.e., motivation can measure both the statistical and semantic significance of an itemset. HM-miner adopts a new pruning strategy, which is based on the motivation upper bound property, to cut down the search space.

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

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.