Database Of Customer Transactions Research Articles

Parallel approaches to extract multi-level high utility itemsets from hierarchical transaction databases

In the field of data mining, high utility itemset mining (HUIM) is a relevant mining task, with the aim of analyzing customer transaction databases. HUIM consists of exploiting the set of items that are often purchased together and yield high profit value. In real-world applications, transaction databases often come with item categorization, stored in a taxonomy. Items in these databases can be clustered into specific categories at higher levels of abstraction. Extracting and analyzing itemsets discovered from different levels of abstraction can provide more useful insights into customer behaviors. However, considering item taxonomy increases the problem’s complexity, hence prolonging the execution time needed to explore the search space. Parallelism is thus employed to address this drawback, but previous approaches are not efficient as they only adopt simple scheduling strategies or do not utilize the full capabilities of a multi-core processor. This work introduces three new efficient strategies to significantly boost the performance of the multi-level high utility itemset mining task using multi-core processing. Two new algorithms, called MCML+ and MCML++, are also proposed by adopting the suggested strategies. Extensive experiments on several large databases show that our proposed algorithms have better performance compared to previous approaches in terms of running time and scalability, up to 4.0 times better than the previous parallelized algorithm, the MCML-Miner algorithm; and over 9.0 times faster than the original sequential algorithm, the MLHUI-Miner algorithm.

Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms’ traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability.

Mining correlated high-utility itemsets using various measures

AbstractDiscovering high-utility itemsets (HUIs) consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining (HUIM) is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in HUIM to find profitable itemsets that are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (fast correlated high-utility itemset miner) is proposed to efficiently discover correlated high-utility itemsets (CHIs). Two versions of the algorithm are proposed: FCHM$_{all\text{-}confidence}$ and FCHM$_{bond}$, which are based on the all-confidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the HUIM literature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly CHIs.

Discovering Periodic Itemsets Using Novel Periodicity Measures

Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxPer (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxPer constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database.

Application of Dynamic Programming Method to Marketing Decisions Based on Customer Database

AbstractThe paper focuses on modeling longitudinal customer behavior and develops a dynamic programming (DP) to show how customer transaction database may be used to guide marketing decisions such as pricing and the design of customer reward programs. Dynamic programming is not as a tool to marketing decisions making in this research but rather as a description of consumer behavior. The results show that the method provides a means for evaluating the effectiveness of marketing strategy, for example, customer reward programs. Moreover, the findings from the model estimation indicate that reward program can actually increase the customer’s purchase level and stimulate the repeat purchase behavior.

A Detection Model of Customer Alive in Information Management Application

In recent years, improvements in information technology have resulted in the increased availability of customer transaction data. This trend is closely linked to an ever-growing desire on the part of the marketing manager to use the firm's customer transaction databases to detect the customer is still alive or has been churn out. This research proposes a stochastic model for information managers to estimate the recency length of customers and predict the next purchase time length after the observation period end. This paper conducts exponetail distribution as interpurchase time and calculates its recency model. The results can provide managers to segment customer or make marketing strategy.

Mining Approach for Updating Sequential Patterns

We are given a large database of customer transactions, where each transaction consists of customer- id, transaction time, and the items bought in the transaction. The discovery of frequent sequences in temporal databases is an important data mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new database. We consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database. In this paper, we propose novel techniques for maintaining sequences in the presence of a) database updates, and b) user interaction (e.g. modifying mining parameters). This is a very challenging task, since such updates can invalidate existing sequences or introduce new ones.

Transition in Time Series Data Mining on Correlated Items

We are given a large database of customer transactions, where each transaction consists of transaction-id, the items bought in the transaction and the transaction time. The whole set of transaction is divided into a number of segments called durations (intervals) based on transaction time. And the dividing standard can be monthly, quarterly or yearly. We introduce the problem of mining strong association rules between consecutive durations using FP-tree and correlation coefficient, which is used to quantitatively describe the strength and sign of a relationship between two variables. This paper deals with the changes in the correlation between any two itemsets at the transition of the consecutive duration. Milestone is a change over point between durations. The transition may be positive or negative which are time points at which the pattern is either positively or negatively correlated. Also the method provides rare items, whose support is poor but are highly correlated.

GCARM A Combined Approach to Data Mining

Mining association rules is an essential task for knowledge discovery. From a large amount of data, potentially useful information may be discovered. Association rules are used to discover the relationships of items or attributes among huge data. These rules can be effective in uncovering unknown relationships, providing results that can be the basis of forecast and decision. The effective management of business is significantly dependent on the quality of its decision making. Past transaction data can be analyzed to discover customer behaviors such that the quality of business decision can be improved. The approach of mining association rules focuses on discovering large itemsets, which are groups of items that appear together in an adequate number of transactions. The proposed method focuses on a combined approach to generate association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and clustering tables and then traverses the graph to generate all large itemsets. The proposed algorithm will outperforms other algorithms which need to make multiple passes over the database.

An efficient approach for the maintenance of user behaviours

Mining sequential patterns is to discover sequential purchasing behaviours for most of the customers from a large number of customer transactions. The strategy of mining sequential patterns focuses on discovering frequent sequences. A frequent sequence is an ordered list of the itemsets purchased by a sufficient number of customers. The previous approaches for mining sequential patterns need to repeatedly scan the database so that they take a large amount of computation time to find frequent sequences. The customer transactions will grow rapidly in a short time, and some of the customer transactions may be antiquated. Consequently, the frequent sequences may be changed due to the insertion of new customer transactions or the deletion of old customer transactions from the database. It may require rediscovering all the patterns by scanning the entire updated customer transaction database. In this paper, we propose an incremental updating technique to maintain the discovered sequential patterns when transactions are inserted into or deleted from the database. Our approach partitions the database into some segments and scans the database segment by segment. For each segment scan, our approach prunes those sequences that cannot be frequent sequences any more to accelerate the finding process of the frequent sequences. Therefore, the number of database scans can be significantly reduced by our approach. The experimental results show that our algorithms are more efficient than other algorithms for the maintenance of mining sequential patterns.

An efficient data mining approach for discovering interesting knowledge from customer transactions

Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interested in some information. Moreover, the criteria of the discovered association rules and sequential patterns for the user requirements may not be the same. Many uninteresting information for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules or sequential patterns to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules and the sequential patterns according to the user requirements.

Mining changes in customer behavior in retail marketing

During the past decade, there have been a variety of significant developments in data mining techniques. Some of these developments are implemented in customized service to develop customer relationship. Customized service is actually crucial in retail markets. Marketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in customer behavior. In the dynamic retail market, understanding changes in customer behavior can help managers to establish effective promotion campaigns. This study integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at different time periods. The proposed approach for mining changes in customer behavior can assist managers in developing better marketing strategies.

Mining interesting sequential patterns for intelligent systems

Mining sequential patterns means to discover sequential purchasing behaviors of most customers from a large number of customer transactions. Past transaction data can be analyzed to discover customer purchasing behaviors such that the quality of business decisions can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the sequential patterns from a large database, and users may be only interested in some sequential patterns. Moreover, the criteria of the discovered sequential patterns for user requirements may not be the same. Many uninteresting sequential patterns for user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only knowledge of interest to them from a large database of customer transactions. In this article, a data mining language is presented. From the data mining language, users can specify the items of interest and the criteria of the sequential patterns to be discovered. Also, an efficient data mining technique is proposed to extract the sequential patterns according to the users' requests. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 73–87, 2005.

Isotonic single-index model for high-dimensional database marketing

While database marketers collect vast amounts of customer transaction data, its utilization to improve marketing decisions presents problems. Marketers seek to extract relevant information from large databases by identifying significant variables and prospective customers. In small databases, they could calibrate logistic regression models via maximum-likelihood methods to determine significant variables and assess customer's response probability. For large databases, however, this approach becomes computationally too intensive to implement in real-time, and so marketers prefer estimation methods that are scalable to high-dimensional databases. In addition, database marketing is practiced in diverse product-markets, and so marketers prefer probability models that are flexible rather than restrict to specific distributions (e.g., logistic). To incorporate scalability and flexibility, we propose isotonic single-index models for database marketing. It furnishes the first projective approximation to a general p-variate function. Its link function is order-preserving (i.e., isotonic), thus encompassing all proper distribution functions. We develop a direct approach for its estimation: we first estimate the orientation of high-dimensional parameter vector without specifying the link function (via sliced inverse regression), and then estimate the non-decreasing link function (via isotonic regression). We illustrate its practical use by analyzing a high-dimensional customer transaction database. This approach yields dimension reduction both column- and row-wise; that is, we not only discover significant variables in a large transaction database, but also prioritize customers into a few distinct groups based on estimated response probability (to enable direct mailing of catalogs).

Mining interesting association rules from customer databases and transaction databases

In this paper, we examine a new data mining issue of mining association rules from customer databases and transaction databases. The problem is decomposed into two subproblems: identifying all the large itemsets from the transaction database and mining association rules from the customer database and the large itemsets identified. For the first subproblem, we propose an efficient algorithm to discover all the large itemsets from the transaction database. Experimental results show that by our approach, the total execution time can be reduced significantly. For the second subproblem, a relationship graph is constructed according to the identified large itemsets from the transaction database and the priorities of condition attributes from the customer database. Based on the relationship graph, we present an efficient graph-based algorithm to discover interesting association rules embedded in the transaction database and the customer database.

Repetition support and mining cyclic patterns

Abstract For customer transaction database, the support (customer support) of the sequential pattern is defined as the fraction of the customers supporting the sequence. We define new forms of the mining patterns, called cyclic patterns , as an extension to the sequential pattern mining by introducing a new parameter, the repetition support . For customer transaction database, the repetition support specifies the minimum number of repetitions of the patterns in each customer transaction sequence. Repeated patterns can also be viewed as cyclic since the beginning of a sequence will follow the end of the previous occurrence of the same sequence. In this paper, we introduce the repetition support parameter, the cyclic pattern mining problem, describe the related algorithms, and at the end of the paper we give some performance results.

Cross-selling through database marketing: a mixed data factor analyzer for data augmentation and prediction

An important aspect of the new orientation on customer relationship marketing is the use of customer transaction databases for the cross-selling of new services and products. In this study, we propose a mixed data factor analyzer that combines information from a survey with data from the customer database on service usage and transaction volume, to make probabilistic predictions of ownership of services with the service provider and with competitors. This data-augmentation tool is more flexible in dealing with the type of data that are usually present in transaction databases. We test the proposed model using survey and transaction data from a large commercial bank. We assume four different types of distributions for the data: Bernoulli for binary service usage items, rank-order binomial for satisfaction rankings, Poisson for service usage frequency, and normal for transaction volumes. We estimate the model using simulated likelihood (SML). The graphical representation of the weights produced by the model provides managers with the opportunity to quickly identify cross-selling opportunities. We exemplify this and show the predictive validity of the model on a hold-out sample of customers, where survey data on service usage with competitors is lacking. We use Gini concentration coefficients to summarize power curves of prediction, which reveals that our model outperforms a competing latent trait model on the majority of service predictions.

Efficient Graph-Based Algorithms for Discovering and Maintaining Association Rules in Large Databases

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items brought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In addition, we extend our revised algorithm to maintain discovered association rules when incremental or decremental updates are made to the databases. Experimental results show the efficiency of our algorithms. The revised algorithm is a significant improvement over the original one on mining association rules. The algorithms for maintaining association rules are more efficient than re-running the mining algorithms for the whole updated database and outperform previously proposed algorithms that need multiple passes over the database.

A graph-based approach for discovering various types of association rules

Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decisions can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large item sets, which are groups of items which appear together in a sufficient number of transactions. We propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large item sets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.

Modeling large data sets in marketing

In the last two decades, marketing databases have grown significantly in terms of size and richness of available information. The analysis of these databases raises several information‐related and statistical issues. We aim at providing an overview of a selection of issues related to the analysis of large data sets. We focus on the two important areas: single source databases and customer transaction databases. We discuss models that have been used to describe customer behavior in these fields. Among the issues discussed are the development of parsimonious models, estimation methods, aggregation of data, data‐fusion and the optimization of customer‐level profit functions. We conclude that problems related to the analysis of large databases are far from resolved, and will stimulate new research avenues in the near future.