An improved design approach in spatial databases using frequent Association Rule Mining algorithm

Association Rule Mining (ARM) is a well recognized and interesting area of research in the field of Data Mining. In ARM, significant amount of research work has been reported. On the contrary, very less work has been reported for Negative Association Rule Mining (NARM). ARM concentrates only on positive rules while NARM explores negative rules. Also, most researchers have worked with discrete transaction dataset. So, in this paper, we propose a technique called Negative and Positive Fuzzy Association Rule Mining (NP-FARM), which mines both negative and positive association rules from a fuzzy transaction dataset. NP-FARM algorithm has been implemented and the experimental results determine the optimal minimum support threshold and optimal minimum confidence threshold for the given dataset. Also, the experimental results demonstrate that, as the size of the dataset increases, a negligible change in execution time is witnessed to mine the growing dataset. Keywords: Association Rule Mining. Fuzzy Transactions, Positive and Negative Item Sets

Association rules mining 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, only the positive association rules are discovered; thirdly, it treat each item with the same frequency although different item may have different frequency. In this paper, we put forward a discovery algorithm for mining positive and negative fuzzy association rules to resolve these three limitations.

The weighted association rules (WARs) mining are made because importance of the items is different. Negative association rules (NARs) play important roles in decision-making. But the misleading rules occur and some rules are uninteresting when discovering positive and negative weighted association rules (PNWARs) simultaneously. So another parameter is added to eliminate the uninteresting rules. A new model in the paper of extending the support-confidence framework with a sliding interest measure could avoid generating misleading rules. An interest measure was defined and added to the mining algorithm for association rules in the model. The interest measure was set according to the demand of users. The experiment demonstrates that the algorithm discovers interesting weighted negative association rules from large database and deletes the contrary rules.

Association rule mining is one of the most popular data mining techniques to find associations among items in a set by mining necessary patterns in a large database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very useful for constructing associative classifiers. In this paper, we propose an algorithm that mines positive and negative association rules without adding any additional measure and extra database scans.

To reduce invalid rules in the mining of association rules, we have analyzed the reasons and presented a relative confidence in the judgment criteria. Based on the value of relative confidence, we classify strong association rules into positive, invalid and negative association rules. We offer an algorithm of mining association rules with new judgment criterion and make tests with Visual FoxPro. The tests indicate that the new method stated in this paper can obviously reduce invalid association rules.

Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data. Rooted in market basket analysis, there are a great number of techniques developed for association rule mining. They include frequent pattern discovery, interestingness, complex associations, and multiple data source mining. This paper introduces the up‐to‐date prevailing association rule mining methods and advocates the mining of complete association rules, including both positive and negative association rules. © 2011 John Wiley & Sons, Inc.WIREs Data Mining Knowl Discov2011 1 97‐116 DOI: 10.1002/widm.10This article is categorized under:Algorithmic Development > Association Rules

Association rule mining is one of the most important data mining techniques. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. All of the literature on negative association mining, to our best knowledge, is confined to the traditional, relatively static database environment, no research work has been conducted on mining negative associations over data streams. In this paper, we propose an algorithm for mining negative associations over data streams. Experiments on the synthetic data stream are performed to show the effectiveness and efficiency of the proposed approach.

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.

The best known and most widely utilized pattern finding algorithm in data mining applications is association rule mining (ARM). Extraction of frequent patterns is an indispensable step in ARM. Most studies in the literature have been implemented on the concept of support and confidence framework utilization. Here, we investigated an efficient and robust ARM scheme based on a self-organizing map (SOM) and an optimized genetic algorithm (OGA). A SOM is an unsupervised neural network that efficaciously produces spatially coordinated internal feature representations and detected abstractions in the input space and is the most efficient clustering technique that reveals conventional similarities in the input space by performing a topology maintaining mapping. Hence, a SOM is utilized to generate accurate clustered frequency patterns and an OGA is used to generate positive and negative association rules with multiple consequences by studying all possible patterns. Experimental analysis on various datasets has shown the robustness of our proposed ARM in comparison to traditional rule mining approaches by proving that a greater number of positive and negative association rules is generated by the proposed methodology resulting in a better performance when compared to conventional rule mining schemes.

Recently, building associative classifiers by miming association rules is a hot research problem. As negative association rules also help to understand the data, in this paper, we present our InterRBF algorithm, which expands RBF kernel into its Maclaurin series, and then mines positive and negative association rules which make great contribution to classification from this series, so as to learn association classifier from the SVM classification model. Taking {0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> as input space, we also show the reasonable value field of hyper-parameter g of RBF kernel by applying the theory of Occam's razor, so as to have good classification performance. Experiment results on 6 UCI datasets show that InterRBF could build associative classifiers with better accuracy and smaller size of rule set than ARC-PAN[1], another associative classifier which is also build with both positive and negative association rules. Furthermore, compared with CMAR [3] and CPAR [4], the average accuracy of InterRBF over the 6 datasets also outperforms the two classifiers.

Modeling microbiome systems as complex networks are known as the problem of network inference. Microbial association network inference is of great significance in applications on clinical diagnosis, disease treatment, pathological analysis, etc. However, most current network inference methods focus on mining strong pairwise associations between microorganisms, which is defective in reflecting the comprehensive interactive patterns participated by multiple microorganisms. It is also possible that the microorganisms involved in the generated network are not dominant in the microbiome due to the mere focus on the strength of pairwise associations. Some scholars tried to mine comprehensive microbial associations by association rule mining methods, but the adopted algorithms are relatively basic and have severe limitations such as low calculation efficiency, lacking the ability of mining negative correlations and high redundancy in results, making it difficult to mine high-quality microbial association rules and accurately infer microbial association networks. We proposed a microbial association network inference method 'MANIEA' based on the improved Eclat algorithm for mining positive and negative microbial association rules. We also proposed a new method for transforming association rules into microbial association networks, which can effectively demonstrate the co-occurrence and causal correlations in association rules. An experiment was conducted on three authentic microbial abundance datasets to compare the 'MANIEA' with currently popular network inference methods, which demonstrated that the proposed 'MANIEA' show advantages in aspects of correlation forms, computation efficiency, adjustability and network characteristics. The algorithms and data are available at: https://github.com/MaidiL/MANIEA.

Mining association rules associates events that took place together. In market basket analysis, these discovered rules associate items purchased together. Items that are not part of a transaction are not considered. In other words, typical association rules do not take into account items that are part of the domain but that are not together part of a transaction. Association rules are based on frequencies and count the transactions where items occur together. However, counting absences of items is prohibitive if the number of possible items is very large, which is typically the case. Nonetheless, knowing the relationship between the absence of an item and the presence of another can be very important in some applications. These rules are called negative association rules. We review current approaches for mining negative association rules and we discuss limitations and future research directions.

Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the mining of positive and negative rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. A number of algorithms are wonder performance but produce large number of negative association rule and also suffered from multi-scan problem. The idea of this paper is to eliminate these problems and reduce large number of negative rules. Hence we proposed an improved approach to mine interesting positive and negative rules based on genetic and MLMS algorithm. In this method we used a multi-level multiple support of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of MLMS and genetic algorithm. This paper proposed a new algorithm (MIPNAR_GA) for mining interesting positive and negative rule from frequent and infrequent pattern sets. The algorithm is accomplished in to three phases: a).Extract frequent and infrequent pattern sets by using apriori method b).Efficiently generate positive and negative rule. c).Prune redundant rule by applying interesting measures. The process of rule optimization is performed by genetic algorithm and for evaluation of algorithm conducted the real world dataset such as heart disease data and some standard data used from UCI machine learning repository.

In view of the forecast of the coal spontaneous combustion, an analysis model to forecast the possibility of coal spontaneous combustion was established. Then, an analysis method of positive and negative association rules based on data mining was proposed. Application of the method can mine rules from the explicit and implicit factors related to coal spontaneous combustion. It can not only overcome the weak point of the traditional association rules method which can only mine the explicit rules, but also output some more meaningful rules, providing scientific basis for controlling coal spontaneous combustion.

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