32724421
An Efficient Algorithm of Frequent Connected Subgraph Extraction
Mining frequent patterns from datasets is one of the key success stories of data mining research. Currently, most of the works focus on independent data, such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to extract frequent patterns from these relations is the objective in this paper. We use graphs to model the relations, and select a simple type for analysis. Combining the graph theory and algorithms to generate frequent patterns, a new algorithm Topology, which can mine these graphs efficiently, has been proposed. We evaluate the performance of the algorithm by doing experiments with synthetic datasets and real data. The experimental results show that Topology can do the job well. At the end of this paper, the potential improvement is mentioned.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
10409272
Repairing Inconsistent XML Documents
XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data.
30881426
Constraint-Based Graph Mining in Large Database
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. C onstr a int- b ased G raph M in ing) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
992965
Infominer: mining surprising periodic patterns
In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considered very significant if its actual occurrence frequency exceeds the prior expectation by a large margin. The traditional metric, such as support, is not necessarily the ideal model to measure this kind of surprising patterns because it treats all patterns equally in the sense that every occurrence carries the same weight towards the assessment of the significance of a pattern regardless of the probability of occurrence. A more suitable measurement, information, is introduced to naturally value the degree of surprise of each occurrence of a pattern as a continuous and monotonically decreasing function of its probability of occurrence. This would allow patterns with vastly different occurrence probabilities to be handled seamlessly. As the accumulated degree of surprise of all repetitions of a pattern, the concept of information gain is proposed to measure the overall degree of surprise of the pattern within a data sequence. The bounded information gain property is identified to tackle the predicament caused by the violation of the downward closure property by the information gain measure and in turn provides an efficient solution to this problem. Empirical tests demonstrate the efficiency and the usefulness of the proposed model.
1459613
PK-tree: A Spatial Index Structure for High Dimensional Point Data
In this chapter we present the PK-tree which is an index structure for high dimensional point data. The proposed indexing structure can be viewed as combining aspects of the PR-quad or K-D tree but where unnecessary nodes are eliminated. The unnecessary nodes are typically the result of skew in the point distribution and we show that by eliminating these nodes the performance of the resulting index is robust to skewed data distributions. The index structure is formally defined, efficiently updateable and bounds on the number of nodes and the mean height of the tree can be proved. Bounds on the expected height of the tree can be given under certain mild constraints on the spatial distribution of points. Empirical evidence both on real data sets and generated data sets shows that the PK-tree outperforms the recently proposed spatial indexes based on the R-tree and X-tree by a wide margin. It is also significant that the relative performance advantage of the PK-tree grows with the dimensionality of the data set.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
12810933
Incremental Subspace Clustering over Multiple Data Streams
Data streams are often locally correlated, with a subset of streams exhibiting coherent patterns over a subset of time points. Subspace clustering can discover clusters of objects in different subspaces. However, traditional sub- space clustering algorithms for static data sets are not readily used for incremental clustering, and is very expensive for frequent re-clustering over dynamically changing stream data. In this paper, we present an efficient incremental sub- space clustering algorithm for multiple streams over sliding windows. Our algorithm detects all the delta-CC-Clusters, which capture the coherent changing patterns among a set of streams over a set of time points. delta-CC'-Cluster s are incrementally generated by traversing a directed acyclic graph pDAG. We propose efficient insertion and deletion operations to update the pDAG dynamically. In addition, effective pruning techniques are applied to reduce the search space. Experiments on real data sets demonstrate the performance of our algorithm.
7938529
Efficient mining of frequent subgraphs in the presence of isomorphism
Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. We propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current start-of-the-art subgraph mining algorithm gSpan.