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

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of tra#c and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system [15].

We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnimaximal frequent itemset, Max-Miner’s output implicitly and concisely represents all frequent itemsets. Max-Miner is shown to result in two or more orders of magnitude in performance improvements over Apriori on some data-sets. On other data-sets where the patterns are not so long, the gains are more modest. In practice, Max-Miner is demonstrated to run in time that is roughly linear in the number of maximal frequent itemsets and the size of the database, irrespective of the size of the longest frequent itemset. tude or more. 1.

Sequences of events describing the behavior and actions of users or systems can be collected in several domains. We consider the problem of discovering frequently occurring episodes in such sequences. An episode is defined to be a collection of events that occur relatively close to each other in a given partial order. Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and present extensive experimental results. The methods are in use in telecommunication alarm management.

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research e orts. The term Web mining has been used intwo distinct ways. The rst, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web usage mining, is the process of mining for user browsing and access patterns. In this paper we de ne Web mining and present an overview of the various research issues, techniques, and development e orts. We brie y describe WEBMINER, a system for Web usage mining, and conclude this paper by listing research issues. 1

Abstract. In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations. All sequences are discovered in only three database scans. Experiments show that SPADE outperforms the best previous algorithm by a factor of two, and by an order of magnitude with some pre-processed data. It also has linear scalability with respect to the number of input-sequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.

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.

Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present TreeMiner, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list. We contrast TreeMiner with a pattern matching tree mining algorithm (PatternMatcher). We conduct detailed experiments to test the performance and scalability of these methods. We find that TreeMiner outperforms the pattern matching approach by a factor of 4 to 20, and has good scaleup properties. We also present an application of tree mining to analyze real web logs for usage patterns.

One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. To prune the exponentially large space of candidates, most existing algorithms, consider only those candidates that have a user defined minimum support. Even with the pruning, the task of finding all association rules requires a lot of computation power and time. Parallel computers offer a potential solution to the computation requirement of this task, provided efficient and scalable parallel algorithms can be designed. In this paper, we present two new parallel algorithms for mining association rules. The Intelligent Data Distribution algorithm efficiently uses aggregate memory of the parallel computer by employing intelligent candi...

Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional