Inter-transaction Association Rules Research Articles

데이터의 의미적 정보를 공정하게 반영한 인터트랜잭션들에 대한 연관규칙 탐사

Recently, to reflect the context between transactions, the intertransaction association rule mining has been study. In this study, we present two problems that is within intertransaction association rule mining method and suggest the methods to solve this problems. First, we suggest an algorithm to reflect changes on data between transactions. Second, we propose the method to solve the unfairly considered frequency of data when intertransactions is generate with transactions. We make more meaningful rules than previous researches. We present the experiment result with measured data from the marine environment.

Mining of Datasets with an Enhanced Apriori Algorithm

Problem statement: Classical association rules are mostly mining intra-transaction associations i.e., associations among items within the same transaction where the idea behind the transaction could be the items bought by the same customer on the same day. The goal of inter-transaction association rules is to represent the associations between various events found in different transactions. Approach: In this study, we break the barrier of transactions and extend the scope of mining association rules from traditional single-dimensional, intratransaction associations to N-Dimensional, inter-transaction associations. With the introduction of dimensional attributes, we lose the luxury of simple representational form of the classical association rules. Mining inter-transaction associations pose more challenges on efficient processing than mining intra-transaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken. Results: Various tests also conducted using the data set collected from different Stock Exchange (SE).Various experimental results are reported by comparing with real life and synthetic datasets and we show the effectiveness of our work in generating rules and in finding acceptable set of rules under varying conditions. Conclusion/Recommendations: This study introduce the notion of N-Dimensional inter-transaction association rule, define its measurements: support and confidence and develop an efficient algorithm called Modified Apriori.

Incremental mining of closed inter-transaction itemsets over data stream sliding windows

Mining inter-transaction association rules is one of the most interesting issues in data mining research. However, in a data stream environment the previous approaches are unable to find the result of the new-incoming data and the original database without re-computing the whole database. In this paper, we propose an incremental mining algorithm, called DSM-CITI (Data Stream Mining for Closed Inter-Transaction Itemsets), for discovering the set of all frequent inter-transaction itemsets from data streams. In the framework of DSM-CITI, a new in-memory summary data structure, ITP-tree, is developed to maintain frequent inter-transaction itemsets. Moreover, algorithm DSM-CITI is able to construct ITP-tree incrementally and uses the property to avoid unnecessary updates. Experimental studies show that the proposed algorithm is efficient and scalable for mining frequent inter-transaction itemsets over stream sliding windows.

Intertransaction Class Association Rule Mining Based on Genetic Network Programming and Its Application to Stock Market Prediction

Intertransaction association rules have been reported to be useful in many fields such as stock market prediction, but still there are not so many efficient methods to dig them out from large data ...

SOFTWARE DEFECT PREDICTION USING INTERTRANSACTION ASSOCIATION RULE MINING

Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.

DETECTION AND PREDICTION OF RARE EVENTS IN TRANSACTION DATABASES

Rare events analysis is an area that includes methods for the detection and prediction of events, e.g. a network intrusion or an engine failure, that occur infrequently and have some impact to the system. There are various methods from the areas of statistics and data mining for that purpose. In this article we propose PREVENT, an algorithm which uses inter-transactional patterns for the prediction of rare events in transaction databases. PREVENT is a general purpose inter-transaction association rules mining algorithm that optimally fits the demands of rare event prediction. It requires only 1 scan on the original database and 2 over the transformed, which is considerably smaller and it is complete as it does not miss any patterns. We provide the mathematical formulation of the problem and experimental results that show PREVENT's efficiency in terms of run time and effectiveness in terms of sensitivity and specificity.

Efficient mining of salinity and temperature association rules from ARGO data

This paper presents an efficient technique for analyzing ARGO ocean data comprising time series of salinity/temperature measurements where informative salinity/temperature patterns are extracted. Most traditional mining techniques focus on finding associations among items within one transaction and are therefore unable to discover rich contextual patterns related to location and time. In order to show the associated salinity/temperature variations among different locations and time intervals, for example, “if the salinity rose from 0.15 psu to 0.25 psu in the area that is in the east–northeast direction and is near Taiwan, then the temperature will rise from 0 °C to 1.2 °C in the area that is in the east–northeast direction and is far away from Taiwan next month”, a quantitative inter-transaction association rules mining algorithm is proposed. The FITI and the PrefixSpan algorithms are adopted to maximize the mining efficiency. The strategy is applied to ocean salinity measurements obtained from the waters surrounding Taiwan. These experimental evaluations show that the proposed algorithm achieves better performance than other inter-transaction association rule mining algorithms.

Recognition of emergent human behaviour in a smart home: A data mining approach

Motivated by a growing need for intelligent housing to accommodate ageing populations, we propose a novel application of intertransaction association rule (IAR) mining to detect anomalous behaviour in smart home occupants. An efficient mining algorithm that avoids the candidate generation bottleneck limiting the application of current IAR mining algorithms on smart home data sets is detailed. An original visual interface for the exploration of new and changing behaviours distilled from discovered patterns using a new process for finding emergent rules is presented. Finally, we discuss our observations on the emergent behaviours detected in the homes of two real world subjects.

Efficient mining of intertransaction association rules

Most of the previous studies on mining association rules are on mining intratransaction associations, i.e., the associations among items within the same transaction. We extend the scope to include multidimensional, intertransaction associations. In a database of stock price information, an example of such an association is "if (company) A's stock goes up on day one, B's stock will go down on day two but go up on day four:" whether we treat company or day as the unit of transaction, the items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple properties in the same rule, so that multidimensional intertransaction associations can also be defined and discovered. Mining intertransaction associations pose more challenges on efficient processing than mining intratransaction associations because the number of potential association rules is extremely large. We introduce the notion of intertransaction association rule and develop an efficient algorithm, FITI (first intra then inter), for mining intertransaction associations, which adopts two major ideas: 1) an intertransaction frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart; and 2) a special data structure is built among intratransaction frequent itemsets for efficient mining of intertransaction frequent itemsets.

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

Clustering is an activity of finding abstractions from data [1]. These abstractions are mainly used for: (i) identifying outliers and (ii) other decision making activities. In this paper, we propose a novel application, association rule mining (ARM), based on abstractions/cluster descriptions obtained using clustering. A majority of ARM algorithms proposed in the literature are used for mining intra-transaction association rules. Typically, the ARM algorithms find large itemsets from the transaction database based on the frequency of co-occurrence of items. ARM involves two main steps [3. A.K.H. Tung, H. Lu, J. Han, L. Feng, Breaking the barrier of transactions: mining inter-transaction association rules, Proceedings of the 1999 International Conference on Knowledge Discovery and Data Mining, August 1999.3]: (i) generating large itemsets which are having the frequency of co-occurrence (support) greater than or equal to the user defined minimum support (σ) and (ii) generation of association rules from the large itemsets which satisfy the user-defined minimum confidence (c). Mining for complete set of inter-transaction association rules along a single dimension, the time axis, is proposed in [3]. In this paper, we propose a clustering scheme based on multiple dimensions for mining a complete set of inter-transaction association rules. This scheme has two components: (i) generation of descriptions of clusters based on multi-dimensional semantic grouping; our algorithm needs at most two database scans to do this step, and (ii) exploring associations between cluster descriptions/abstractions.2. Clustering process Clustering is a subjective process. It employs knowledge to group data. Knowledge is in the form of the similarity measure used, the values assigned to parameters like the number of clusters and assumptions on the nature of clusters, and structures that capture explicit knowledge [1]. We call clustering based on knowledge, semantic clustering. In this paper, we discuss clustering based on multiple dimensions including size of transaction, cost of transaction, and their combinations to generate inter-transaction associations.

Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data

Inter-transactional association rules, first presented in our early work [H. Lu, J. Han, L. Feng, Stock movement prediction and n-dimensional inter-transaction association rules, in: Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, pp. 12:1–12:7; H. Lu, L. Feng, J. Han, ACM Trans. Inf. Syst. 18 (4) (2000) 423–454], give a more general view of association relationships among items. Two kinds of algorithms, named Extended/Extended Hash-based Apriori (E/EH-Apriori) [Lu et al. (1998, 2000), loc. cit.] and First-Intra-Then-Inter (FITI) [K. H. Tung, H. Lu, J. Han, L. Feng, Breaking the barrier of transactions: Mining inter-transaction association rules, in: Proceedings ACM SIGKDD International Conference Knowledge Discovery and Data Mining, USA, August 1999, pp. 297–301], were presented for mining inter-transactional association rules from large data sets. A template-guided constraint-based inter-transactional association mining method was described in [L. Feng, H. Lu, J. Yu, J. Han, Mining inter-transaction association rules with templates, in: Proceedings ACM CIKM International Conference Information and Knowledge Management, USA, November 1999, pp. 225–233]. The current paper extends our previous work substantially in both theoretical and practical aspects. In the theoretical aspects, we improve the inter-transactional association rule framework by giving a more concise definition of inter-transactional association rules and related measurements, and investigate the closure property, theoretical foundations, multi-dimensional mining contexts, and performance issues in mining such extended association rules. We study the downward closure property problem within the inter-transactional association rule framework, and propose a solution for efficient mining of inter-transactional association rules. A set of examples, lemmas and theorems are provided to verify our discussions. We also present a hole-catered extended Apriori algorithm for mining inter-transactional association rules. Different from our previous work, here, we take data holes that possibly exist in the mining contexts into consideration. We also address some important technical issues, including correctness, termination and computational complexity, in this paper. In practice, we study the applicability of inter-transactional association rules to weather prediction, using multi-station meteorological data obtained from the Hong Kong Observatory headquarters. We report our experimental results as well as the experiences gained during the weather study. In particular, the deficiency of the current support/confidence-based association mining framework and its further extension in providing multi-dimensional predictive capabilities are addressed. These extensions significantly augment the theory and practicality of the more general inter-transactional association rules. It is our hope that the work reported here could stimulate further interest not only in the applications of association rule techniques to non-transactional real-world data under multi-dimensional contexts, but also in the relevant theoretical and performance issues of association rule techniques.

Beyond intratransaction association analysis

In this paper, we extend the scope of mining association rules from traditional single-dimensional intratransaction associations, to multidimensional intertransaction associations. Intratransaction associations are the associations among items with thesame transaction, where the notion of the transaction could be the items bought by thesame customer, the events happened on thesame day, and so on. However, an intertransaction association describes the association relationships amongdifferent transactions, such as “if(company) A's stock goes up on day 1, B's stock will go down on day 2, but go up on day 4.” In this case, whether we treat company or day as the unit of transaction, the associated items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple contextual properties in the same rule, so thatmultidimensionalintertransaction associations can be defined and discovered. A two-dimensional intertransaction association rule example is“After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away,”which involves two dimensions:time and space. Mining intertransaction associations poses more challenges on efficient processing than mining intratransaction associations. Interestingly, intratransaction association can be treated as a special case of intertransaction association from both a conceptual and algorithmic point of view. In this study, we introduce the notion of multidimensional intertransaction association rules, study their measurements—supportand confidence—and develop algorithms for mining intertransaction associations by extension of Apriori. We overview our experience using the algorithms on both real-life and synthetic data sets. Further extensions of multidimensional intertransaction association rules and potential applications are also discussed.

Generalized multidimensional association rules

The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of research activities around this problem. Traditional association rule mining is limited to intra-transaction. Only recently the concept ofN-dimensional inter-transaction association rule (NDITAR) was proposed by H.J. Lu. This paper modifies and extends Lu’s definition of NDITAR based on the analysis of its limitations, and the generalized multidimensional association rule (GMDAR) is subsequently introduced, which is more general, flexible and reasonable than NDITAR.