Large Database Of Customer Transactions Research Articles

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 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 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.

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.

Adaptive Fraud Detection

One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.

Mining association rules between sets of items in large databases

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.