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

We introduce the Iceberg-CUBE problem as a reformulation of the datacube (CUBE) problem. The Iceberg-CUBE problem is to compute only those group-by partitions with an aggregate value (e.g., count) above some minimum support threshold. The result of Iceberg-CUBE can be used (1) to answer group-by queries with a clause such as HAVING COUNT(*) ?= X, where X is greater than the threshold, (2) for mining multidimensional association rules, and (3) to complement existing strategies for identifying interesting subsets of the CUBE for precomputation. We present a new algorithm (BUC) for Iceberg-CUBE computation. BUC builds the CUBE bottom-up; i.e., it builds the CUBE by starting from a group-by on a single attribute, then a group-by on a pair of attributes, then a group-by on three attributes, and so on. This is the opposite of all techniques proposed earlier for computing the CUBE, and has an important practical advantage: BUC avoids computing the larger group-bys that do not meet minimum sup...

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

Constraint-based rule miners find all rules in a given dataset meeting user-specified constraints such as minimum support and confidence. We describe a new algorithm that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational data). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of “frequent itemsets”.

Partial periodicity search, i.e., search for partial periodic patterns in time-series databases, is an interesting data mining problem. Previous studies on periodicity search mainly consider finding full periodic patterns, where every point in time contributes (precisely or approximately) to the periodicity. However, partial periodicity is very common in practice since it is more likely that only some of the time episodes may exhibit periodic patterns. We present several algorithms for efficient mining of partial periodic patterns, by exploring some interesting properties related to partial periodicity, such as the Apriori property and the max-subpattern hit set property, and by shared mining of multiple periods. The max-subpattern hit set property is a vital new property which allows us to derive the counts of all frequent patterns from a relatively small subset of patterns existing in the time series. We show that mining partial periodicity needs only two scans over the time series database, even for mining multiple periods. The performance study shows our proposed methods are very efficient in mining long periodic patterns.

Association rules are a fundamental class of patterns that exist in data. The key strength of association rule mining is its completeness. It finds all associations in the data that satisfy the user specified minimum support and minimum confidence constraints. This strength, however, comes with a major drawback. It often produces a huge number of associations. This is particularly true for data sets whose attributes are highly correlated. The huge number of associations makes it very difficult, if not impossible, for a human user to analyze in order to identify those interesting/useful ones. In this paper, we propose a novel technique to overcome this problem. The technique first prunes the discovered associations to remove those insignificant associations, and then finds a special subset of the unpruned associations to form a summary of the discovered associations. We call this subset of associations the direction setting (DS) rules as they set the directions that are followed by the...

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.

Recent work has highlighted the importance of the constraint-based mining paradigm in the context of frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases. In this paper, we study constraints which cannot be handled with existing theory and techniques. For example,, , ( can contain items of arbitrary values) "!$ # %'&) ( , are customarily regarded as “tough ” constraints in that they cannot be pushed inside an algorithm such as Apriori. We develop a notion of convertible constraints and systematically analyze, classify, and characterize this class. We also develop techniques which enable them to be readily pushed deep inside the recently developed FP-growth algorithm for frequent itemset mining. Results from our detailed experiments show the effectiveness of the techniques developed. 1.

Association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in reallife applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. T...

Abstract. Given a large collection of transactions containing items, a basic common data mining problem is to extract the so-called frequent itemsets (i.e., sets of items appearing in at least a given number of transactions). In this paper, we propose a structure called free-sets, from which we can approximate any itemset support (i.e., the number of transactions containing the itemset) and we formalize this notion in the framework of ɛ-adequate representations (H. Mannila and H. Toivonen, 1996. In Proc. of the Second International Conference on Knowledge Discovery and Data Mining (KDD’96), pp. 189–194). We show that frequent free-sets can be efficiently extracted using pruning strategies developed for frequent itemset discovery, and that they can be used to approximate the support of any frequent itemset. Experiments on real dense data sets show a significant reduction of the size of the output when compared with standard frequent itemset extraction. Furthermore, the experiments show that the extraction of frequent free-sets is still possible when the extraction of frequent itemsets becomes intractable, and that the supports of the frequent free-sets can be used to approximate very closely the supports of the frequent itemsets. Finally, we consider the effect of this approximation on association rules (a popular kind of patterns that can be derived from frequent itemsets) and show that the corresponding errors remain very low in practice.