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.

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a-novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.

We present our perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology. We describe three classes of database mining problems involving classification, associations, and sequences, and argue that these problems can be uniformly viewed as requiring discovery of rules embedded in massive data. We describe a model and some basic operations for the process of rule discovery. We show how the database mining problems we consider map to this model and how they can be solved by using the basic operations we propose. We give an example of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm not only is efficient in discovering classification rules but also has accuracy comparable to ID3, one of the current best classifiers. Index Terms. database mining, knowledge discovery, classification, associations, sequences, decision trees Current address: Computer Science De...

Classification is an important data mining problem. Although classification is a well-studied problem, most of the current classi-fication algorithms require that all or a por-tion of the the entire dataset remain perma-nently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algo-rithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data min-ing. 1

We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task pipelining and dynamic load balancing to yield faster implementations. The task parallel approach uses dynamic subtree partitioning among processors. Our performance evaluation shows that the construction of a decision-tree classifier can be effectively parallelized on an SMP machine with good speedup. 1

Classi cation is an important data mining problem. Although classi cation is a wellstudied problem, most of the current classication algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classi cation algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalabilityaswell. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining. 1