258373
Preserving Private Knowledge in Frequent Pattern Mining
The knowledge discovered by data mining may contain sensitive information, which may cause potential threats towards privacy and security. In this paper, we address the problem of better preserving private knowledge by proposing an Item-based Pattern Sanitization to prevent the disclosure of private patterns. We also present two strategies to generate a safe and shareable pattern set for preserving private knowledge in frequent pattern mining
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.
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.
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.
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.
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.
884789
A Fast Algorithm for Approximate Quantiles in High Speed Data Streams
We present a fast algorithm for computing approximate quantiles in high speed data streams with deterministic error bounds. For data streams of size N where N is unknown in advance, our algorithm partitions the stream into sub-streams of exponentially increasing size as they arrive. For each sub-stream which has a fixed size, we compute and maintain a multi-level summary structure using a novel algorithm. In order to achieve high speed performance, the algorithm uses simple block-wise merge and sample operations. Overall, our algorithms for fixed-size streams and arbitrary-size streams have a computational cost of O(N log(1/epsivlogepsivN)) and an average per-element update cost of O(log logN) if epsiv is fixed.
2673667
ApproxMAP: Approximate Mining of Consensus Sequential Patterns
Conventional sequential pattern mining methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns approximately shared by many sequences. In this paper, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. We present an efficient and effective algorithm, ApproxMAP, to mine consensus patterns from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We use a real case study to illustrate the effectiveness of ApproxMAP.
3544821
11,001 New Features for Statistical Machine Translation
We use the Margin Infused Relaxed Algorithm of Crammer et al. to add a large number of new features to two machine translation systems: the Hiero hierarchical phrase-based translation system and our syntax-based translation system. On a large-scale Chinese-English translation task, we obtain statistically significant improvements of +1.5 Bleu and + 1.1 Bleu, respectively. We analyze the impact of the new features and the performance of the learning algorithm.
3069611
A General Framework for Fast Co-clustering on Large Datasets Using Matrix Decomposition
Simultaneously clustering columns and rows (co- clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and n columns, the time complexity of these methods is usually in the order of mtimesn (if not higher). This limits their applicability to data matrices involving a large number of columns and rows. Moreover, an implicit assumption made by existing co-clustering methods is that the whole data matrix needs to be held in the main memory. In this paper, we propose a general framework, CRD, for co-clustering large datasets utilizing recently developed sampling- based matrix decomposition methods. The time complexity of our approach is linear in m and n. And it does not require the whole data matrix be in the main memory. Experimental results show that CRD achieves competitive accuracy to existing co-clustering methods but with much less computational cost.
2987996
Mining patterns in long sequential data with noise
Pattern discovery in time series data has been a problem of great importance in many fields, e.g., computational biology, performance analysis, consumer behavior, etc. Recently, considerable amount of research has been carried out in this area. The facts that the input data is typically very large and noises may present in various formats pose great challenge to the mining process. Recently, we have made several new research advances in this area. In this paper, we present some of them. We will survey new models proposed to address different types of noises as well as scalable algorithms developed for efficiently mining patterns under each model.