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48
Mining significant graph patterns by leap search
 in SIGMOD ’08
"... With everincreasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with userspecified objective functions. Most objective functions are not antimonotonic, which could fail all of frequencycentric graph mining algorithms. In thi ..."
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Cited by 41 (14 self)
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With everincreasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with userspecified objective functions. Most objective functions are not antimonotonic, which could fail all of frequencycentric graph mining algorithms. In this paper, we give the first comprehensive study on general mining method aiming to find most significant patterns directly. Our new mining framework, called LEAP(Descending Leap Mine), is developed to exploit the correlation between structural similarity and significance similarity in a way that the most significant pattern could be identified quickly by searching dissimilar graph patterns. Two novel concepts, structural leap search and frequency descending mining, are proposed to support leap search in graph pattern space. Our new mining method revealed that the widely adopted branchandbound search in data mining literature is indeed not the best, thus sketching a new picture on scalable graph pattern discovery. Empirical results show that LEAP achieves orders of magnitude speedup in comparison with the stateoftheart method. Furthermore, graph classifiers built on mined patterns outperform the uptodate graph kernel method in terms of efficiency and accuracy, demonstrating the high promise of such patterns.
Assessing data mining results via swap randomization
 ACM Transactions on Knowledge Discovery from Data
"... The problem of assessing the significance of data mining results on highdimensional 0–1 data sets has been studied extensively in the literature. For problems such as mining frequent sets and finding correlations, significance testing can be done by, e.g., chisquare tests, or many other methods. H ..."
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Cited by 37 (6 self)
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The problem of assessing the significance of data mining results on highdimensional 0–1 data sets has been studied extensively in the literature. For problems such as mining frequent sets and finding correlations, significance testing can be done by, e.g., chisquare tests, or many other methods. However, the results of such tests depend only on the specific attributes and not on the dataset as a whole. Moreover, the tests are more difficult to apply to sets of patterns or other complex results of data mining. In this paper, we consider a simple randomization technique that deals with this shortcoming. The approach consists of producing random datasets that have the same row and column margins with the given dataset, computing the results of interest on the randomized instances, and comparing them against the results on the actual data. This randomization technique can be used to assess the results of many different types of data mining algorithms, such as frequent sets, clustering, and rankings. To generate random datasets with given margins, we use variations of a Markov chain approach, which is based on a simple swap operation. We give theoretical results on the efficiency of different randomization methods, and apply the swap randomization method to several wellknown datasets. Our results indicate that for some datasets the structure discovered by the data mining algorithms is a random artifact, while for other datasets the discovered structure conveys meaningful information.
Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining
 Journal of Machine Learning Research
"... This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use ..."
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Cited by 29 (0 self)
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This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.
A SelfTraining Approach for Resolving Object Coreference on the Semantic Web
, 2011
"... An object on the Semantic Web is likely to be denoted with multiple URIs by different parties. Object coreference resolution is to identify “equivalent ” URIs that denote the same object. Driven by the Linking Open Data (LOD) initiative, millions of URIs have been explicitly linked with owl:sameAs s ..."
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Cited by 15 (0 self)
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An object on the Semantic Web is likely to be denoted with multiple URIs by different parties. Object coreference resolution is to identify “equivalent ” URIs that denote the same object. Driven by the Linking Open Data (LOD) initiative, millions of URIs have been explicitly linked with owl:sameAs statements, but potentially coreferent ones are still considerable. Existing approaches address the problem mainly from two directions: one is based upon equivalence inference mandated by OWL semantics, which finds semantically coreferent URIs but probably omits many potential ones; the other is via similarity computation between propertyvalue pairs, which is not always accurate enough. In this paper, we propose a selftraining approach for object coreference resolution on the Semantic Web, which leverages the two classes
From Local Patterns to Global Models: The LeGo Approach to Data Mining
"... Abstract. In this paper we present LeGo, a generic framework that utilizes existing local pattern mining techniques for global modeling in a variety of diverse data mining tasks. In the spirit of well known KDD process models, our work identifies different phases within the data mining step, each of ..."
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Cited by 13 (5 self)
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Abstract. In this paper we present LeGo, a generic framework that utilizes existing local pattern mining techniques for global modeling in a variety of diverse data mining tasks. In the spirit of well known KDD process models, our work identifies different phases within the data mining step, each of which is formulated in terms of different formal constraints. It starts with a phase of mining patterns that are individually promising. Later phases establish the context given by the global data mining task by selecting groups of diverse and highly informative patterns, which are finally combined to one or more global models that address the overall data mining task(s). The paper discusses the connection to various learning techniques, and illustrates that our framework is broad enough to cover and leverage frequent pattern mining, subgroup discovery, pattern teams, multiview learning, and several other popular algorithms. The Safarii learning toolbox serves as a proofofconcept of its high potential for practical data mining applications. Finally, we point out several challenging open research questions that naturally emerge in a constraintbased localtoglobal pattern mining, selection, and combination framework. 1
Tell Me Something I Don’t Know: Randomization Strategies for Iterative Data Mining
"... There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however, leads to the problem of whether the results found by one method are a reflection of the phenomenon shown by the results ..."
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Cited by 9 (0 self)
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There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however, leads to the problem of whether the results found by one method are a reflection of the phenomenon shown by the results of another method, or whether the results depict in some sense unrelated properties of the data. For example, using clustering can give indication of a clear cluster structure, and computing correlations between variables can show that there are many significant correlations in the data. However, it can be the case that the correlations are actually determined by the cluster structure. In this paper, we consider the problem of randomizing data so that previously discovered patterns or models are taken into account. The randomization methods can be used in iterative data mining. At each step in the data mining process, the randomization produces random samples from the set of data matrices satisfying the already discovered patterns or models. That is, given a data set and some statistics (e.g., cluster centers or cooccurrence counts) of the data, the randomization methods sample data sets having similar values of the given statistics as the original data set. We use Metropolis sampling based on local swaps to achieve this. We describe experiments on real data that demonstrate the usefulness of our approach. Our results indicate that in many cases, the results of, e.g., clustering actually imply the results of, say, frequent pattern discovery.
Directly Mining Descriptive Patterns
 SIAM SDM
, 2012
"... Mining small, useful, and highquality sets of patterns has recently become an important topic in data mining. The standard approach is to first mine many candidates, and then to select a good subset. However, the pattern explosion generates such enormous amounts of candidates that by postprocessin ..."
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Cited by 8 (2 self)
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Mining small, useful, and highquality sets of patterns has recently become an important topic in data mining. The standard approach is to first mine many candidates, and then to select a good subset. However, the pattern explosion generates such enormous amounts of candidates that by postprocessing it is virtually impossible to analyse dense or large databases in any detail. We introduce Slim, an anytime algorithm for mining highquality sets of itemsets directly from data. We use MDL to identify the best set of itemsets as that set that describes the data best. To approximate this optimum, we iteratively use the current solution to determine what itemset would provide most gain— estimating quality using an accurate heuristic. Without requiring a premined candidate collection, Slim is parameterfree in both theory and practice. Experiments show we mine highquality pattern sets; while evaluating ordersofmagnitude fewer candidates than our closest competitor, Krimp, we obtain much better compression ratios—closely approximating the locallyoptimal strategy. Classification experiments independently verify we characterise data very well. 1
Finding Subgroups having Several Descriptions: Algorithms for Redescription Mining
"... Given a 01 dataset, we consider the redescription mining task introduced by Ramakrishnan, Parida, and Zaki. The problem is to find subsets of the rows that can be (approximately) defined by at least two different Boolean formulae on the attributes. That is, we search for pairs (α, β) ofBoolean form ..."
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Cited by 6 (2 self)
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Given a 01 dataset, we consider the redescription mining task introduced by Ramakrishnan, Parida, and Zaki. The problem is to find subsets of the rows that can be (approximately) defined by at least two different Boolean formulae on the attributes. That is, we search for pairs (α, β) ofBoolean formulae such that the implications α → β and β → α both hold with high accuracy. We require that the two descriptions α and β are syntactically sufficiently different. Such pairs of descriptions indicate that the subset has different definitions, a fact that gives useful information about the data. We give simple algorithms for this task, and evaluate their performance. The methods are based on pruning the search space of all possible pairs of formulae by different accuracy criteria. The significance of the findings is tested by using randomization methods. Experimental results on simulated and real data show that the methods work well: on simulated data they find the planted subsets, and on real data they produce small and understandable results. 1
SelfSufficient Itemsets: An Approach to Screening Potentially Interesting Associations Between Items
"... Selfsufficient itemsets are those whose frequency cannot explained solely by the frequency of either their subsets or of their supersets. We argue that itemsets that are not selfsufficient will often be of little interest to the data analyst, as their frequency should be expected once that of the ..."
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Cited by 5 (0 self)
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Selfsufficient itemsets are those whose frequency cannot explained solely by the frequency of either their subsets or of their supersets. We argue that itemsets that are not selfsufficient will often be of little interest to the data analyst, as their frequency should be expected once that of the itemsets on which their frequency depends is known. We present statistical tests for statistically sound discovery of selfsufficient itemsets, and computational techniques that allow those tests to be applied as a postprocessing step for any itemset discovery algorithm. We also present a measure for assessing the degree of potential interest in an itemset that complements these statistical measures.