Mining cyclic patterns with multiple minimum repetition supports

The alarm correlation analysis based on multi-level fuzzy association rules mining is the cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, multi-level fuzzy association rules mining algorithms are proposed, and two strategies are adopted to set minimum support, which are multiple minimum supports and one minimum support. Simulations are carried out to the comparison of algorithms under the two strategies. Multi-level fuzzy association rules mining of alarms is effectively realized. The advantages and efficiency of algorithms are demonstrated by the experiments.

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in the data mining community. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover real informative rules. How to reduce the response time for each remining process thus becomes a crucial issue. In this paper, we examine the problem of maintaining the discovered multi-supported generalized association rules when the multiple minimum support constraint is refined and propose a novel algorithm called RGA_MSR to accomplish the work. By keeping and utilizing the set of frequent itemsets and negative border, and adopting vertical intersection counting strategy, the proposed RGA_MSR algorithm can significantly reduce the computation time spent on rediscovery of frequent itemsets and has very good performance.

Frequent graph mining allows us to find useful frequent sub-graph patterns from large and complicated graph databases. In a lot of real world applications, graph patterns with relatively low supports can be used as meaningful information. However, previous methods based on a single minimum support threshold have trouble finding them. That is, they cause “rare item problem”, which means that useful sub-graphs with low supports cannot be extracted when a minimum support threshold is high, while an enormous number of patterns have to be mined to obtain these useful ones when the value is low. To overcome this problem, we propose a novel algorithm, FGM-MMS (Frequent Graph Mining based on Multiple Minimum Support constraints). After that, we demonstrate that the suggested algorithm outperforms a state-of-the-art graph mining algorithm through comprehensive performance experiments.

Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which have three limitations. Firstly, it can not concern quantitative attributes; secondly, only direct sequential patterns are discovered; thirdly, it can not process these data items with similar frequencies which will result in the dilemma called the rare item problem. In this paper, we put forward a discovery algorithm for mining both direct and indirect fuzzy sequential patterns with multiple minimum supports by combining these three extensions.

Social networks such as Twitter provide thousands of terabytes per day, which can be exploited to find relevant information. This relevant information is used to promote marketing strategies, analyze current political issues, and track market trends, to name a few examples. One instance of relevant information is finding cyclic behavior patterns (i.e., patterns that frequently repeat themselves over time) in the population. Because trending topics on Twitter change rapidly, efficient algorithms are required, especially when considering location and time (i.e., the specific location and time) during broadcasts. This article presents an efficient algorithm based on association rules to find cyclical patterns on Twitter, considering the inherent spatio-temporal attributes of data. Using a Hash Table enhances the efficiency of this algorithm, called HashCycle. Notably, HashCycle does not use minimum support and can detect patterns in a single run over a sequence. The processing times of HashCycle were compared to the Apriori (which is a well-known and widely used on diverse platforms) and Projection-based Partial Periodic Patterns (PPA) algorithms (which is one of the most efficient algorithms in terms of processing times). Empirical results from two spatio-temporal databases (a synthetic data set and one based on Twitter) show that HashCycle has more efficient processing times than two state-of-the-art algorithms: Apriori and PPA.

Efficient mining of generalized association rules with non-uniform minimum support

Techniques for mining rare patterns have been researched in the association rule mining area because traditional frequent pattern mining methods have to generate a large amount of unnecessary patterns in order to find rare patterns from large databases. One such technique, the multiple minimum support threshold framework was devised to extract rare patterns by using a different minimum item support threshold for each item in a database. Nevertheless, this framework cannot sufficiently reflect environments of the real world. The reason is that it does not consider weights of items, such as market prices of products and fatality rates of diseases, in its mining process. Therefore, an algorithm has been proposed to mine rare patterns with utilities exceeding a user-specified minimum utility by considering rarity and utility information of items. However, since this algorithm employs the concept of traditional high utility pattern mining, patterns' lengths are not considered for determining utilities of the patterns. If the length of a pattern is sufficiently long, the pattern is more likely to have an enough utility to become a high utility pattern regardless of item utilities within the pattern. Therefore, the algorithm cannot guarantee that all items in a mined pattern have high utilities. In this paper, we propose a novel algorithm that effectively reduces such dependency of patterns on their lengths by considering their lengths in the mining process in order to mine more meaningful rare patterns compared to patterns mined by previous algorithms. Experimental results demonstrate that our algorithm extracts a lesser number of more meaningful patterns and consumes less computational resources compared to state-of-the-art algorithms.

Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism

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.

Association rule mining is an important machine learning tool for unveiling critical biological relations between genes from omics data. Previous approaches typically are designed for one single genomic dataset, and most of them use a single minimum support threshold globally. To overcome the above two general limitations, in this work, we present a novel Transcriptomic and Proteomic Rule Mining (TrapRM) method using Weighted Shortest Distance based Multiple Minimum Supports for Multi-Omics Dataset that integrates gene expression, methylation and protein-protein interaction data. To do so, we initially introduce three new thresholds: Weighted Shortest Distance based Multiple Minimum Supports (WSDMS), Weighted Shortest Distance based Multiple Minimum Confidences (WSDMC), and Weighted Shortest Distance based Multiple Minimum Lifts (WSDML). Our algorithm is superior to the related existing algorithms since it generates substantially fewer number of rules and smaller average weighted shortest distance value than the existing methods. Finally, our TrapRM algorithm is useful for extracting the rules that are critical for translational and clinical applications when being applied to drug or disease related multi-omics data.

Forecasting for a time series signal carrying single pattern characteristics can be done properly using function mapping-based principle by a well-designed artificial neural network model. But the performances degraded very much when time series carried the mixture of different patterns characteristics. The level of difficulty increases further when there is a need to predict far time samples. Among several possible mixtures of patterns, the trend-cycle time series is having its importance because of its occurrence in many real-life applications like in electric power generation, fuel consumption and automobile sales. Over the mixed characteristics of patterns, a neural model, suffered heavily in getting generalized learning, in result poor performances appeared over test data. To overcome this issue in this work, a decomposition-based approach has been applied to separate the component patterns of trend and cyclic patterns, and a dedicated model has been developed for predicting the individual data patterns. The linear characteristic of the trend data pattern has been modeled through a linear regression model while the nonlinearity behavior of cyclic pattern has been model by an adaptive radial basis function neural network. The final predicted outcome has been considered as the linear combination of individual model outcomes. The Gaussian function has been considered as the kernel function in the radial basis function neural network because of its wider and efficient applicability in function mapping. The performance of the neural model has been improved very much by providing the adaptive value of spreads and centers of basis function along with weights values. In this paper, two different applications of forecasting in the area of electric power demand by the individual house and month-wise annual power generation have been considered. Based on house characteristics parameters, the power demanded by a house have been considered which carried a moderate complexity of function mapping problem while in another case, total power generation needed to be predicted on the monthly basis for a year from just the previous year observation, which carried the mixed behavior of trend and cyclic pattern. For house power demand forecasting the adaptive kernel-based radial basis function has shown very satisfactory performances and much better against static kernel radial basis function and multilayer perceptron neural network. The integrated approach of neural model and linear regression has shown very efficient outcomes for the mixture pattern while individual neural models were failed to do so. The coefficient of determination (R2) has been applied to estimate the quality of predicted outcomes and comparison.

Semi-supervised learning using frequent itemset and ensemble learning for SMS classification

Mining for association rules and frequent patterns is a central activity in data mining. However, most existing algorithms are only moderately suitable for real-world scenarios. Most strategies use parameters like minimum support, for which it can be very difficult to define a suitable value for unknown datasets. Since most untrained users are unable or unwilling to set such technical parameters, we address the problem of replacing the minimum-support parameter with top- n strategies. In our paper, we start by extending a top- n implementation of the ECLAT algorithm to improve its performance by using heuristic search strategy optimizations. Also, real-world datasets are often distributed and modern database architectures are switching from expensive SMPs to cheaper shared-nothing blade servers. Thus, most mining queries require distribution handling. Since partitioning can be forced by user-defined semantics, it is often forbidden to transform the data. Therefore, we developed an adaptive top- n frequent-pattern mining algorithm that simplifies the mining process on real distributions by relaxing some requirements on the results. We first combine the PARTITION and the TPUT algorithms to handle distributed top- n frequent-pattern mining. Then, we extend this new algorithm for distributions with real-world data characteristics. For frequent-pattern mining algorithms, equal distributions are important conditions, and tiny partitions can cause performance bottlenecks. Hence, we implemented an approach called MAST that defines a minimum absolute-support threshold. MAST prunes patterns with low chances of reaching the global top- n result set and high computing costs. In total, our approach simplifies the process of frequent-pattern mining for real customer scenarios and data sets. This may make frequent-pattern mining accessible for very new user groups. Finally, we present results of our algorithms when run on the SAP NetWeaver BW Acceleratorwith standard and real business datasets.

PT Astra International BMW Semarang is a company operating in the automotive sector with 3 supporting pillars, namely Sales, Aftersales and Spare Parts for BMW car units. The availability of spare parts is one of the determining factors for consumer satisfaction with the company because if the spare parts stock is empty it will cause consumer disappointment with the company. By using spare parts sales transaction data for the period January 2019 – June 2023, totaling 52,162, it will be utilized using data mining association techniques with the a priori algorithm and the eclat algorithm. The problem in this research is how to find out consumer purchasing patterns so that there is no shortage or empty stock of spare parts in the warehouse. This research aims to determine the association of spare parts purchasing patterns in sales transactions so that partman get recommendations in making decisions about providing priority types of spare parts. This research methodology uses CRISP-DM (Cross-Industry Standard Process for Data Mining) and is implemented with the R programming language with R studio software. In 3 trials using the Apriori algorithm and 3 trials with the Eclat algorithm, The result with the highest confidence appears in a combination of 3 itemsets with minimum support 0.01 and confidence 0.9, namely if a customer buys B11.42.8.593.186 (Set oil-filter Mx) and B83.12.5.A1A.683 (Washer Cleaner) then they will also buy Z99000000333 ( BMW Engine Oil) with confidence 1.00 or 100%. From the results of this association's analysis, it can be used as advice for the management of PT Astra International BMW Semarang in managing spare parts stock.

One of the sub areas of the data mining includes sequential pattern mining. This mining algorithm is to find the repeating patterns after mining the sequence databases. These are used to find the relation between the various items in the data for different purposes. As these data keep changing according to the change in time, mining should be done on incremented or updated database to obtain the frequent sequential patterns. The proposed algorithm in this paper uses modified algorithm of sequential pattern mining including concepts of fuzzy space intervals. In this algorithm, frequently occurring sequential patterns in the sequence database are mined using apriori like method. Fuzzy theory is used for mining the space interval between the frequently occurring sequences. The sequentially occurring candidate patterns are found first. After that follows the frequently occurring sequential patterns, which are found by calculating the minimum fuzzy support along with the use of the fuzzy number. Each space cluster is found by fuzzy support computation. The final results comprises the frequently occurring fuzzy space sequentially based patterns. At last the outcome also confirms the excellence of the suggested MISPFSI algorithm.