Data Mining: An Overview from a Database Perspective <s> Parallel data mining <s> 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. <s> BIB001 </s> Data Mining: An Overview from a Database Perspective <s> Parallel data mining <s> In this paper we introduce generalized projections (G P an extension of duplicateeliminating projections, that capture aggregations, groupbys, duplicate-eliminating projections (distinctand duplicate-preserving projections in a common unified framework. Using G P s we extend well known and simple algorithms for SQL queries that use distinct projections to derive algorithms for queries using aggregations like sum, max, min, count, and avg. We develop powerful query rewrite rules for aggregate queries that unify and extend rewrite rules previously known in the literature. We then illustrate the power of our approach by solving a very practical and important problem in data warehousing: how to answer an aggregate query on base tables using materialized aggregate views (summary <s> BIB002 </s> Data Mining: An Overview from a Database Perspective <s> Parallel data mining <s> The topic of data warehousing encompasses architectures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse, suitable for direct querying or analysis. In recent years data warehousing has become a prominent buzzword in the database industry, but attention from the database research community has been limited. In this paper we motivate the concept of a data warehouse, we outline a general data warehousing architecture, and we propose a number of technical issues arising from the architecture that we believe are suitable topics for exploratory research. <s> BIB003 </s> Data Mining: An Overview from a Database Perspective <s> Parallel data mining <s> Efficient processing of aggregation queries is essential for decision support applications. This paper describes a class of query transformations, called eager aggregation and laty aggregation, that allows a query optimizer to move group-by operations up and down the query tree. Eager aggregation partially pushes a groupby past a join. After a group-by is partially pushed down, we still need to perform the original groupby in the upper query block. Eager aggregation reduces the number of input rows to the join and thus may result in a better overall plan. The reverse transformation, lazy aggregation, pulls a group-by above a join and combines two group-by operations into one. This transformation is typically of interest when an aggregation query references a grouped view (a view containing a groupby). Experimental results show that the technique is very beneficial for queries in the TPC-D benchmark. <s> BIB004
It is noted that data mining in general requires progressive knowledge collection and revision based on a huge transaction database. How to achieve e cient parallel data mining is a very challenging issue, since, with the transaction database being partitioned across all nodes, the amount of inter-node data transmission required for reaching global decisions can be prohibitively large, thus signi cantly compromising the bene t achievable from parallelization. For example, in a shared nothing type parallel environment like SP2 44], each node can directly collect information only from its local database partition, and the process to reach a global decision from partial knowledge collected at individual nodes could itself be complicated and communication intensive. An algorithm for parallel data mining, called PDM, was reported in 67]. Under PDM, with the entire transaction database being partitioned across all nodes, each node will employ a hashing method to identify candidate k-itemsets (i.e., itemsets consisting of k items) from its local database. To reduce the communication cost incurred, a clue-and-poll technique was devised in 67] to resolve the uncertainty due to the partial knowledge collected at each node by judiciously selecting a small fraction of the itemsets for the information exchange among nodes. BIB001 Multi-level Data Generalization, Summarization, and Characterization Data and objects in databases often contain detailed information at primitive concept levels. For example, the \item" relation in a \sales" database may contain attributes about the primitive level item information such as item number, item name, date made, price, etc. It is often desirable to summarize a large set of data and present it at a high concept level. For example, one may like to summarize a large set of the items related to some sales to give a general description. This requires an important functionality in data mining: data generalization. Data generalization is a process which abstracts a large set of relevant data in a database from a low concept level to relatively high ones. The methods for e cient and exible generalization of large data sets can be categorized into two approaches: (1) data cube approach BIB002  BIB003 BIB004 , and (2) attribute-oriented induction approach 37, 40].