Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree- based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. TherefS3A we have developed techniques fi discovering workflow models. Starting pointfS such techniques is a so-called "workflow log" containinginfg3SfiHfl" about the workflow process as it is actually being executed. We present a new algorithm to extract a process modelf3q such a log and represent it in terms of a Petri net. However, we will also demonstrate that it is not possible to discover arbitrary workflow processes. In this paper we explore a classof workflow processes that can be discovered. We show that the #-algorithm can successfqFS mine any workflow represented by a so-called SWF-net. Key words: Workflow mining, Workflow management, Data mining, Petri nets. 1

Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of which may substantially reduce the number of combinations to be examined. However, still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long.

Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an efficient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single prefix path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is efficient and scalable over large databases, and is faster than the previously proposed methods. 1 Introduction It has been well recognized that frequent pattern minin...

One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for finding all such descriptions. We give bounds for the number of database accesses that the algorithm makes. For this, we introduce the concept of the border of a theory, a notion that turns out to be surprisingly powerful in analyzing the algorithm. We also consider the verification problem of a KDD process: given r and a set of sentences S ` L, determine whether S is exactly the set of interesting statements about r. We show strong connections between the verification problem and the hypergraph transversal problem. The verification problem arises in a natural way when using sampling to speed up the pattern discovery step in KDD.

We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule such as "a period of low telephone call activity is usually followed by a sharp rise ill call vohune". Examples of rules relating two or more time series are "if the Microsoft stock price goes up and lntel falls, then IBM goes up the next. day, " and "if Microsoft goes up strongly fro " one day, then declines strongly on the next day, and on the same days Intel stays about, level, then IBM stays about level. " Our emphasis is in the discovery of local patterns in multivariate time series, in contrast to traditional time series analysis which largely focuses on global models. Thus, we search for rules whose conditions refer to patterns in time series. However, we do not want to define beforehand which patterns are to be used; rather, we want the patterns to be formed fl’om the data in the context of rule discovery. We describe adaptive methods for finding rules of the above type fi’om time-series data. The methods are based on discretizing the sequence hy methods resembling vector quantization. \,Ve first form subsequences by sliding window through the time series, and then cluster these subsequences by using a suitable measure of time-series similarity. The discretized version of the time series is obtained by taldng the cluster identifiers corresponding to the subsequence. Once tl,e time-series is discretized, we use simple rule finding methods to obtain rifles from the sequence. "vVe present empMcal resuh.s on the behavior of the method.

With the explosive growth of data available on the World Wide Web, discovery and analysis of useful information from the World Wide Web becomes a practical necessity.Web access pattern, which is the sequence of accesses pursued by users frequently, is a kind of interesting and useful knowledge in practice. In this paper, we study the problem of mining access patterns from Web logs efficiently. A novel data structure, called Web access pattern tree, or WAP-tree in short, is developed for efficient mining of access patterns from pieces of logs. The Web access pattern tree stores highly compressed, critical information for access pattern mining and facilitates the developmentofnovel algorithms for mining access patterns in large set of log pieces. Our algorithm can find access patterns from Web logs quite efficiently. The experimental and performance studies show that our method is in general an order of magnitude faster than conventional methods.

Today there are several efficient algorithms that cope with the popular and computationally expensive task of association rule mining. Actually, these algorithms are more or less described on their own. In this paper we explain the fundamentals of association rule mining and moreover derive a general framework. Based on this we describe today's approaches in context by pointing out common aspects and differences. After that we thoroughly investigate their strengths and weaknesses and carry out several runtime experiments. It turns out that the runtime behavior of the algorithms is much more similar as to be expected.

The discovery of the relationships between chemical structure and biological function is central to biological science and medicine. In this paper we apply data mining to the problem of predicting chemical carcinogenicity. This toxicology application was launched at IJCAI'97 as a research challenge for artificial intelligence. Our approach to the problem is descriptive rather than based on classification; the goal being to find common substructures and properties in chemical compounds, and in this way to contribute to scientific insight. This approach contrasts with previous machine learning research on this problem, which has mainly concentrated on predicting the toxicity of unknown chemicals. Our contribution to the field of data mining is the ability to discover useful frequent patterns that are beyond the complexity of association rules or their known variants. This is vital to the problem, which requires the discovery of patterns that are out of the reach of simple transformations...

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