OpenRaiser/Intern-Atlas huggingface dataset:
Semantic relation extraction with kernels over typed dependency trees
Authors: Frank Reichartz, Hannes Korte, Gerhard Paass
Venue: KDD
Year: 2010

An important step for understanding the semantic content of text is the extraction of semantic relations between entities in natural language documents. Automatic extraction techniques have to be able to identify different versions of the same relation which usually may be expressed in a great variety of ways. Therefore these techniques benefit from taking into account many syntactic and semantic features, especially parse trees generated by automatic sentence parsers. Typed dependency parse trees are edge and node labeled parse trees whose labels and topology contains valuable semantic clues. This information can be exploited for relation extraction by the use of kernels over structured data for classification. In this paper we present new tree kernels for relation extraction over typed dependency parse trees. On a public benchmark data set we are able to demonstrate a significant improvement in terms of relation extraction quality of our new kernels over other state-of-the-art kernels.
DUST: a generalized notion of similarity between uncertain time series
Authors: Smruti R. Sarangi, Karin Murthy
Venue: KDD
Year: 2010

Large-scale sensor deployments and an increased use of privacy-preserving transformations have led to an increasing interest in mining uncertain time series data. Traditional distance measures such as Euclidean distance or dynamic time warping are not always effective for analyzing uncertain time series data. Recently, some measures have been proposed to account for uncertainty in time series data. However, we show in this paper that their applicability is limited. In specific, these approaches do not provide an intuitive way to compare two uncertain time series and do not easily accommodate multiple error functions.
Connecting the dots between news articles
Authors: Dafna Shahaf, Carlos Guestrin
Venue: KDD
Year: 2010

The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today's society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture.
Mixture models for learning low-dimensional roles in high-dimensional data
Authors: Manas Somaiya, Christopher M. Jermaine, Sanjay Ranka
Venue: KDD
Year: 2010

Archived data often describe entities that participate in multiple roles. Each of these roles may influence various aspects of the data. For example, a register transaction collected at a retail store may have been initiated by a person who is a woman, a mother, an avid reader, and an action movie fan. Each of these roles can influence various aspects of the customer's purchase: the fact that the customer is a mother may greatly influence the purchase of a toddler-sized pair of pants, but have no influence on the purchase of an action-adventure novel. The fact that the customer is an action move fan and an avid reader may influence the purchase of the novel, but will have no effect on the purchase of a shirt.
Mining uncertain data with probabilistic guarantees
Authors: Liwen Sun, Reynold Cheng, David W. Cheung, Jiefeng Cheng
Venue: KDD
Year: 2010

Data uncertainty is inherent in applications such as sensor monitoring systems, location-based services, and biological databases. To manage this vast amount of imprecise information, probabilistic databases have been recently developed. In this paper, we study the discovery of frequent patterns and association rules from probabilistic data under the Possible World Semantics. This is technically challenging, since a probabilistic database can have an exponential number of possible worlds. We propose two effcient algorithms, which discover frequent patterns in bottom-up and top-down manners. Both algorithms can be easily extended to discover maximal frequent patterns. We also explain how to use these patterns to generate association rules. Extensive experiments, using real and synthetic datasets, were conducted to validate the performance of our methods.
Extracting temporal signatures for comprehending systems biology models
Authors: Naren Sundaravaradan, K. S. M. Tozammel Hossain, Vandana Sreedharan, Douglas J. Slotta, John Paul C. Vergara, Lenwood S. Heath, Naren Ramakrishnan
Venue: KDD
Year: 2010

Systems biology has made massive strides in recent years, with capabilities to model complex systems including cell division, stress response, energy metabolism, and signaling pathways. Concomitant with their improved modeling capabilities, however, such biochemical network models have also become notoriously complex for humans to comprehend. We propose network comprehension as a key problem for the KDD community, where the goal is to create explainable representations of complex biological networks. We formulate this problem as one of extracting temporal signatures from multi-variate time series data, where the signatures are composed of ordinal comparisons between time series components. We show how such signatures can be inferred by formulating the data mining problem as one of feature selection in rank-order space. We propose five new feature selection strategies for rank-order space and assess their selective superiorities. Experimental results on budding yeast cell cycle models demonstrate compelling results comparable to human interpretations of the cell cycle.
k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining
Authors: Chih-Hua Tai, Philip S. Yu, Ming-Syan Chen
Venue: KDD
Year: 2010

For any outsourcing service, privacy is a major concern. This paper focuses on outsourcing frequent itemset mining and examines the issue on how to protect privacy against the case where the attackers have precise knowledge on the supports of some items. We propose a new approach referred to as k-support anonymity to protect each sensitive item with k-1 other items of similar support. To achieve k-support anonymity, we introduce a pseudo taxonomy tree and have the third party mine the generalized frequent itemsets under the corresponding generalized association rules instead of association rules. The pseudo taxonomy is a construct to facilitate hiding of the original items, where each original item can map to either a leaf node or an internal node in the taxonomy tree. The rationale for this approach is that with a taxonomy tree, the k nodes to satisfy the k-support anonymity may be any k nodes in the taxonomy tree with the appropriate supports. So this approach can provide more candidates for k-support anonymity with limited fake items as only the leaf nodes, not the internal nodes, of the taxonomy tree need to appear in the transactions. Otherwise for the association rule mining, the k nodes to satisfy the k-support anonymity have to correspond to the leaf nodes in the taxonomy tree. This is far more restricted. The challenge is thus on how to generate the pseudo taxonomy tree to facilitate k-support anonymity and to ensure the conservation of original frequent itemsets. The experimental results showed that our methods of k-support anonymity can achieve very good privacy protection with moderate storage overhead.
Mass estimation and its applications
Authors: Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, James Swee Chuan Tan
Venue: KDD
Year: 2010

This paper introduces mass estimation--a base modelling mechanism in data mining. It provides the theoretical basis of mass and an efficient method to estimate mass. We show that it solves problems very effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as good as and often better than a total of eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.
UP-Growth: an efficient algorithm for high utility itemset mining
Authors: Vincent S. Tseng, Cheng-Wei Wu, Bai-En Shie, Philip S. Yu
Venue: KDD
Year: 2010

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose an efficient algorithm, namely UP-Growth (Utility Pattern Growth), for mining high utility itemsets with a set of techniques for pruning candidate itemsets. The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans of the database. The performance of UP-Growth was evaluated in comparison with the state-of-the-art algorithms on different types of datasets. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions.
Active learning for biomedical citation screening
Authors: Byron C. Wallace, Kevin Small, Carla E. Brodley, Thomas A. Trikalinos
Venue: KDD
Year: 2010

Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe a real-world, deployed application of AL to the problem of biomedical citation screening for systematic reviews at the Tufts Medical Center's Evidence-based Practice Center. We propose a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features)and extend this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features. Our methods outperform existing AL strategies on three real-world systematic review datasets. We argue that evaluation must be specific to the scenario under consideration. To this end, we propose a new evaluation framework for finite-pool scenarios, wherein the primary aim is to label a fixed set of examples rather than to simply induce a good predictive model. We use a method from medical decision theory for eliciting the relative costs of false positives and false negatives from the domain expert, constructing a utility measure of classification performance that integrates the expert preferences. Our findings suggest that the expert can, and should, provide more information than instance labels alone. In addition to achieving strong empirical results on the citation screening problem, this work outlines many important steps for moving away from simulated active learning and toward deploying AL for real-world applications.
An efficient causal discovery algorithm for linear models
Authors: Zhenxing Wang, Laiwan Chan
Venue: KDD
Year: 2010

Bayesian network learning algorithms have been widely used for causal discovery since the pioneer work [13,18]. Among all existing algorithms, three-phase dependency analysis algorithm (TPDA) [5] is the most efficient one in the sense that it has polynomial-time complexity. However, there are still some limitations to be improved. First, TPDA depends on mutual information-based conditional independence (CI) tests, and so is not easy to be applied to continuous data. In addition, TPDA uses two phases to get approximate skeletons of Bayesian networks, which is not efficient in practice. In this paper, we propose a two-phase algorithm with partial correlation-based CI tests: the first phase of the algorithm constructs a Markov random field from data, which provides a close approximation to the structure of the true Bayesian network; at the second phase, the algorithm removes redundant edges according to CI tests to get the true Bayesian network. We show that two-phase algorithm with partial correlation-based CI tests can deal with continuous data following arbitrary distributions rather than only Gaussian distribution.
Unsupervised transfer classification: application to text categorization
Authors: Tianbao Yang, Rong Jin, Anil K. Jain, Yang Zhou, Wei Tong
Venue: KDD
Year: 2010

We study the problem of building the classification model for a target class in the absence of any labeled training example for that class. To address this difficult learning problem, we extend the idea of transfer learning by assuming that the following side information is available: (i) a collection of labeled examples belonging to other classes in the problem domain, called the auxiliary classes; (ii) the class information including the prior of the target class and the correlation between the target class and the auxiliary classes. Our goal is to construct the classification model for the target class by leveraging the above data and information. We refer to this learning problem as unsupervised transfer classification. Our framework is based on the generalized maximum entropy model that is effective in transferring the label information of the auxiliary classes to the target class. A theoretical analysis shows that under certain assumption, the classification model obtained by the proposed approach converges to the optimal model when it is learned from the labeled examples for the target class. Empirical study on text categorization over four different data sets verifies the effectiveness of the proposed approach.
Collusion-resistant privacy-preserving data mining
Authors: Bin Yang, Hiroshi Nakagawa, Issei Sato, Jun Sakuma
Venue: KDD
Year: 2010

Recent research in privacy-preserving data mining (PPDM) has become increasingly popular due to the wide application of data mining and the increased concern regarding the protection of private and personal information. Lately, numerous methods of privacy-preserving data mining have been proposed. Most of these methods are based on an assumption that semi-honest is and collusion is not present. In other words, every party follows such protocol properly with the exception that it keeps a record of all its intermediate computations without sharing the record with others. In this paper, we focus our attention on the problem of collusions, in which some parties may collude and share their record to deduce the private information of other parties. In particular, we consider a general problem in PPDM - multiparty secure computation of some functions of secure summations of data spreading around multiple parties. To solve such a problem, we propose a new method that entails a high level of security - full-privacy. With this method, no sensitive information of a party will be revealed even when all other parties collude. In addition, this method is efficient with a running time of O(m). We will also show that by applying this general method, a large number of problems in PPDM can be solved with enhanced security.
Feature selection for support vector regression using probabilistic prediction
Authors: Jian-Bo Yang, Chong Jin Ong
Venue: KDD
Year: 2010

This paper presents a novel wrapper-based feature selection method for Support Vector Regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiment shows that the proposed method generally performs better, and at least as well as the existing methods, with notable advantage when the data set is sparse.
Automatic malware categorization using cluster ensemble
Authors: Yanfang Ye, Tao Li, Yong Chen, Qingshan Jiang
Venue: KDD
Year: 2010

In this paper, resting on the analysis of instruction frequency and function-based instruction sequences, we develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. We propose a principled cluster ensemble framework for combining individual clustering solutions based on the consensus partition. The domain knowledge in the form of sample-level constraints can be naturally incorporated in the ensemble framework. In addition, to account for the characteristics of feature representations, we propose a hybrid hierarchical clustering algorithm which combines the merits of hierarchical clustering and k-medoids algorithms and a weighted subspace K-medoids algorithm to generate base clusterings. The categorization results of our AMCS system can be used to generate signatures for malware families that are useful for malware detection. The case studies on large and real daily malware collection from Kingsoft Anti-Virus Lab demonstrate the effectiveness and efficiency of our AMCS system.
Large linear classification when data cannot fit in memory
Authors: Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Venue: KDD
Year: 2010

Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data larger than the memory capacity due to the random access to the disk. We propose and analyze a block minimization framework for data larger than the memory size. At each step a block of data is loaded from the disk and handled by certain learning methods. We investigate two implementations of the proposed framework for primal and dual SVMs, respectively. As data cannot fit in memory, many design considerations are very different from those for traditional algorithms. Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method.
Document clustering via dirichlet process mixture model with feature selection
Authors: Guan Yu, Rui-zhang Huang, Zhaojun Wang
Venue: KDD
Year: 2010

One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we propose a novel approach, namely DPMFS, to address this issue. The proposed approach is designed 1) to group documents into a set of clusters while the number of document clusters is determined by the Dirichlet process mixture model automatically; 2) to identify the discriminative words and separate them from irrelevant noise words via stochastic search variable selection technique. We explore the performance of our proposed approach on both a synthetic dataset and several realistic document datasets. The comparison between our proposed approach and stage-of-the-art document clustering approaches indicates that our approach is robust and effective for document clustering.
Multi-label learning by exploiting label dependency
Authors: Min-Ling Zhang, Kun Zhang
Venue: KDD
Year: 2010

In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the conditional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other well-established methods.
Using data mining techniques to address critical information exchange needs in disaster affected public-private networks
Authors: Li Zheng, Chao Shen, Liang Tang, Tao Li, Steven Luis, Shu-Ching Chen, Vagelis Hristidis
Venue: KDD
Year: 2010

Crisis Management and Disaster Recovery have gained immense importance in the wake of recent man and nature inflicted calamities. A critical problem in a crisis situation is how to efficiently discover, collect, organize, search and disseminate real-time disaster information. In this paper, we address several key problems which inhibit better information sharing and collaboration between both private and public sector participants for disaster management and recovery. We design and implement a web based prototype implementation of a Business Continuity Information Network (BCIN) system utilizing the latest advances in data mining technologies to create a user-friendly, Internet-based, information-rich service and acting as a vital part of a company's business continuity process. Specifically, information extraction is used to integrate the input data from different sources; the content recommendation engine and the report summarization module provide users personalized and brief views of the disaster information; the community generation module develops spatial clustering techniques to help users build dynamic community in disasters. Currently, BCIN has been exercised at Miami-Dade County Emergency Management.
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Authors: Jun Zhu, Ni Lao, Eric P. Xing
Venue: KDD
Year: 2010

Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automatically discover the inherent structures underlying complex data. Both problems can be cast as solving an l1-norm regularized parameter estimation problem. The existing Grafting method can avoid doing inference on dense graphs in structure learning by incrementally selecting new features. However, Grafting performs a greedy step to optimize over free parameters once new features are included. This greedy strategy results in low efficiency when parameter learning is itself non-trivial, such as in MRFs, in which parameter learning depends on an expensive subroutine to calculate gradients. The complexity of calculating gradients in MRFs is typically exponential to the size of maximal cliques.