Mining Association Rules on Time-sensitive Data

Recently, with companies and government agencies saving large repositories of time stream/temporal data, there is a large push for adapting association rule mining algorithms for dynamic, targeted querying. In addition, issues with data processing latency and results depreciating in value with the passage of time, create a need for swifter and more efficient processing. The aim of targeted association mining is to find potentially interesting implications in large repositories of data. Using targeted association mining techniques, specific implications that contain items of user interest can be found faster and before the implications have depreciated in value beyond usefulness. In this paper, the DynTARM algorithm is proposed for the discovery of targeted and rare association rules. DynTARM has the flexibility to discover strong and rare association rules from data streams within the user's sphere of interest. By introducing a measure, called the Volatility Index, to assess the fluctuation in the confidence of rules, rules conforming to different temporal patterns are discovered.

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.

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Mining encompasses various algorithms such as clustering, classification, and association rule mining. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(association rules mining in novel genetic algorithm), Compared to the algorithm in [Agrawal R, et al., Mining association rules between sets of items in large databases, 1993], the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.

PRS64 PATIENT CHARACTERISTICS ASSOCIATED WITH HOSPITALIZATION THROUGH EMERGENCY DEPARTMENT VISIT WITH ASTHMA EXACERBATION: ASSOCIATION RULE MINING APPROACH

In recent years, association rule mining is exploring its popularity in the rule mining research communities. In this paper, we present the taxonomy of association patterns analysis approaches in depth. The association patterns analysis is based on support constraints, interestingness, and patterns dimension from different levels of abstraction. An in-depth research statistical view is presented in each category of the association patterns analysis research. Among the identified association rule mining research categories, we have selected rare pattern mining for further exploration of our study. Analysis of rare patterns is very much helpful in determining the rare relationships between patterns. Also, we will identify novel rare data patterns analysis techniques to solve the various problems in real-life applications. The overall research impact in the association rule mining research domain can be found in this review article. It will focus on various research scopes and the future directions of rule mining as well as rare pattern mining research.

This research demonstrates the application of association rule mining to spatio‐temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio‐temporal association rule occurs when there is a spatio‐temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)‐based data pre‐processing is used to integrate diverse data sets, extract spatio‐temporal relationships, classify numeric data into ordinal categories, and encode spatio‐temporal relationship data in tabular format for use by conventional (non‐spatio‐temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio‐temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.

Multi-dimensional semantic clustering of large databases for association rule mining

Privacy is very important in Data Mining. Association rule mining, is a important technique, has already been applied in a wide range of areas. Large databases cannot be secured by encryption or other techniques because of complexities. These security issues can be divided into two categories: data hiding and knowledge hiding. This paper deals with the problem of association rule mining which preserves the confidentiality of each database i.e. Knowledge hiding. Knowledge hiding is concerned with the sanitization of confidential knowledge from the data. There are many methods to solve this problem. The one, presented in the paper is called support-based and confidence-based blocking schemes. Apriori algorithm is used for mining rule from database. Knowledge hiding is achieved using ISL and DSR. The improvement over sensitive rule hiding is proposed to offer more accuracy and security with smaller negative impact. In ever changing business environment like enterprises e-businesses, the old fashioned disclosure and database inference protection techniques are not capable to ensure complete data privacy. Privacy is important for the on line disclosure of private information Organizations should be able to evaluate the risk of disclosing information and should adopt new more efficient approaches for information disclosure control, in order to maintain their competitive edge in the market. Not only the data but also the hidden knowledge in the data should be made secure. The sensitive information can be extracted in the form of association rules with association rule mining tools. But this can jeopardize the privacy of the customers. Some other rule could be very critical for the company itself such as buying patterns of very rich customers. For example, an Internet based company may give up selling hardware and may concentrate on selling books and videos. Therefore a rule relating the customer buying patterns of hardware may no longer be sensitive for that company. It is desirable to apply a trivial algorithm that hides data by deleting it randomly. Data Mining is the process of discovering new patterns from large data sets involving methods from statistics and artificial intelligence but also database management. Privacy has become an important issue in Data Mining. Many methods have been brought out to solve this problem. This paper focuses on the problem of association rule mining, which hides the knowledge from each database. This paper reviews the major method of knowledge hiding. Association rule mining, as a very important technique, has already been applied in a wide range of areas. Knowledge hiding is concerned with the sanitization of confidential knowledge from the data. As a result of association rule mining, many useful association rules will be discovered, but at the same time, many privacy rules will also be exposed which do not want others to know. To solve this, limit the mining process, in order to keep these sensitive rules being hidden. There are so many methods to solve this problem. The one that is covered in the paper is just one kind of them, called support-based and confidence-based blocking schemes. Algorithm used For Knowledge Hiding is ISL algorithm or DSR algorithm (2). The main disadvantage of a block in algorithm is the fact that the dataset, apart from the blocked values, is not distorted. Thus, an adversary can disclose the hidden rules by identifying those generating item sets that contain question marks and lead to rules with a maximum confidence that lies above the minimum confidence threshold. If the number of these rules is small then the probability of identifying the sensitive ones among them becomes high. To avoid this problem, a method for selectively removing individual values from a database is introduced to prevent the discovery of a set of rules, while preserving the data for other applications. The problem of building privacy preserving algorithms is considered for one category of data mining techniques, the association rule mining. New metrics is introduced in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining (1).

Rare association rule mining has received a great deal of attention in the past few years. In this chapter, the authors propose a multi methodological approach to the problem of rare association rule mining that integrates three different strands of research in this area. Firstly, the authors make use of statistical techniques such as the Fisher test to determine whether itemsets co-occur by chance or not. Secondly, they use clustering as a pre-processing technique to improve the quality of the rare rules generated. Their third strategy is to weigh itemsets to ensure upward closure, thus checking unbounded growth of the rule base. Their results show that clustering isolates heterogeneous segments from each other, thus promoting the discovery of rules which would otherwise remain undiscovered. Likewise, the use of itemset weighting tends to improve rule quality by promoting the generation of rules with rarer itemsets that would otherwise not be possible with a simple weighting scheme that assigns an equal weight to all possible itemsets. The use of clustering enabled us to study in detail an important sub-class of rare rules, which we term absolute rare rules. Absolute rare rules are those are not just rare to the dataset as a whole but are also rare to the cluster from which they are derived.

We present a method that applies association rule mining for information retrieval. Our approach is different from traditional information retrieval since retrieval is done based on association rather than similarity, which might be useful for knowledge discovery purposes such as finding an explanation or elaboration for an event in a collection of domain-specific documents. The method proposed in this paper is based on the SmoothApriori algorithm which accommodates similarity in the association rule mining process to mine association rules between sentences or larger text units. We introduce query-focused association rule mining that allows association-based retrieval from larger amount of data than with a traditional association-rule mining approach. Combined with SmoothApriori, query-focused association rule mining provides association-based retrieval for textual data. This new method was evaluated on the task of automatically restoring sentences that were artificially removed from aviation investigation reports and showed significantly better results than any of our similarity-based retrieval baselines.

Correlated protein-DNA interaction (binding cores) between transcription factor (TFs) and transcription factor binding sites (TFBSs) are usually identified by costly 3D structural experiments. To avoid numerous unsuccessful trials, we are motivated to develop a cheap and efficient sequence-based computational method for providing testable novel binding cores with high confidence to accelerate the experiments. Although there are abundant sequence-based motif discovery algorithms, few directly address associating both TF and TFBS core motifs which are both verifiable on 3D structures. In this paper, we formally define the problem of discovering correlated TF-TFBS binding cores, and apply association rule mining techniques over existing real sequence data (TRANSFAC). The proposed algorithm first builds two frequent sequence tree (FS-Tree) structures storing condensed information for association rule mining. Association rules are then generated by depth-first traversal on the structures. FS-Trees have several advantages to support further applications, including efficient calculation of the support and confidence, simple generation of candidate rules, and applicability of effective pruning techniques. As a result, the FS-Trees serve as a useful basis for more general extensions related to biological binding core identification. We tested our algorithm on real sequence data from the biological database TRANSFAC and focus on efficiency comparisons with the recent work employing association rule mining. The rules discovered reveal real TF-TFBS binding cores in independent 3D verifications on Protein Data Bank (PDB).KeywordsBioinformaticsassociation rule miningsequencealgorithm

Association analysis is an important task in data analysis to find all co-occurrence relationships (i.e., frequent itemsets or confident association rules) from the transactional dataset. An association rule can help people better discover patterns and develop corresponding strategies. The process of data analysis can be highly summarized as a set of queries, where each query is a real-valued function of the dataset. However, without any restriction and protection, accessing the dataset to answer the queries may lead to the disclosure of individual privacy. In this paper, we propose and implement the association rule mining with differential privacy algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items, and uses random truncation and uniform partition to lower the dimensionality of the dataset. We also stabilize the noise scale by adaptively allocating the privacy budgets, and bound the overall privacy loss. In addition, we prove that the association rule mining with differential privacy algorithm satisfies ex post differential privacy, and verify the utility of our association rule mining with differential privacy algorithm through a series of experiments. To the best of our knowledge, our work is the first differentially private association rule mining algorithm under multiple support thresholds.

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.

The visual professional education of existing colleges and universities can no longer meet the needs of the development of the times. In view of such a situation, this paper explores the integration and development of visual professional education and book creative design using association rule mining. Based on xAPI technology, a data collection platform has been designed to provide security, easy scalability, and real-time data acquisition for visual professional education and creative design text data acquisition. Afterward, the data is preprocessed so that it can be used for association rule mining in the future. The support degree, confidence degree, and enhancement degree of the two are defined to achieve the purpose of constructing the Apriori association rule mining analysis model for the fusion development of visual professional education and book and cultural and creative design. The analysis of the association rules of visual education and creative design shows that 10 association rules are obtained when the minimum support degree is 0.4. The minimum confidence degree is 0.6, and the confidence degree of the practical activities in visual professional education for the cultural elements and creative elements in book and creative design is 0.657, 0.619. The degree of enhancement is 0.709 and 1.021, and the correlation rules of all professional education and book and creative design show a synchronous enhancement of the correlation rules. Association rules show a synchronized enhancement of correlation.

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.