OpenRaiser/Intern-Atlas huggingface dataset:
A Bayesian network classifier with inverse tree structure for voxelwise magnetic resonance image analysis
Authors: Rong Chen, Edward Herskovits
Venue: KDD
Year: 2005

We propose a Bayesian-network classifier with inverse-tree structure (BNCIT) for joint classification and variable selection. The problem domain of voxelwise magnetic-resonance image analysis often involves millions of variables but only dozens of samples. Judicious variable selection may render classification tractable, avoid over-fitting, and improve classifier performance. BNCIT embeds the variable-selection process within the classifier-training process, which makes this algorithm scalable. BNCIT is based on a Bayesian-network model with inverse-tree structure, i.e., the class variable C is a leaf node, and predictive variables are parents of C; thus, the classifier-training process returns a parent set for C, which is a subset of the Markov blanket of C. BNCIT uses voxels in the parent set, and voxels that are probabilistically equivalent to them, as variables for classification of new image data. Since the data set has a limited number of samples, we use the jackknife method to determine whether the classifier generated by BNCIT is a statistical artifact. In order to enhance stability and improve classification accuracy, we model the state of the probabilistically equivalent voxels with a latent variable. We employ an efficient method for determining states of hidden variables, thus reducing dramatically the computational cost of model generation. Experimental results confirm the accuracy and efficiency of BNCIT.
Failure detection and localization in component based systems by online tracking
Authors: Haifeng Chen, Guofei Jiang, Cristian Ungureanu, Kenji Yoshihira
Venue: KDD
Year: 2005

The increasing complexity of today's systems makes fast and accurate failure detection essential for their use in mission-critical applications. Various monitoring methods provide a large amount of data about system's behavior. Analyzing this data with advanced statistical methods holds the promise of not only detecting the errors faster, but also detecting errors which are difficult to catch with current monitoring tools. Two challenges to building such detection tools are: the high dimensionality of observation data, which makes the models expensive to apply, and frequent system changes, which make the models expensive to update. In this paper, we present algorithms to reduce the dimensionality of data in a way that makes it easy to adapt to system changes. We decompose the observation data into signal and noise subspaces. Two statistics, the Hotelling T2 score and squared prediction error (SPE) are calculated to represent the data characteristics in signal and noise subspaces respectively. Instead of tracking the original data, we use a sequentially discounting expectation maximization (SDEM) algorithm to learn the distribution of the two extracted statistics. A failure event can then be detected based on the abnormal change of the distribution. Applying our technique to component interaction data in a simple e-commerce application shows better accuracy than building independent profiles for each component. Additionally, experiments on synthetic data show that the detection accuracy is high even for changing systems.
Web mining from competitors' websites
Authors: Xin Chen, Yi-fang Brook Wu
Venue: KDD
Year: 2005

This paper presents a framework for user-oriented text mining. It is then illustrated with an example of discovering knowledge from competitors' websites. The knowledge to be discovered is in the form of association rules. A user's background knowledge is represented as a concept hierarchy developed from documents on his/her own website. The concept hierarchy captures the semantic usage of words and relationships among words in background documents. Association rules are identified among the noun phrases extracted from documents on competitors' websites. The interestingness measure, i.e. novelty, which measures the semantic distance between the antecedent and the consequent of a rule in the background knowledge, is computed from the co-occurrence frequency of words and the connection lengths among words in the concept hierarchy. A user evaluation of the novelty of discovered rules demonstrates that the correlation between the algorithm and the human judges is comparable to that between human judges.
Fast window correlations over uncooperative time series
Authors: Richard Cole, Dennis E. Shasha, Xiaojian Zhao
Venue: KDD
Year: 2005

Data arriving in time order (a data stream) arises in fields including physics, finance, medicine, and music, to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramatically as sensor technology improves. Further, the number of sensors is increasing, so correlating data between sensors becomes ever more critical in order to distill knowlege from the data. In many applications such as finance, recent correlations are of far more interest than long-term correlation, so correlation over sliding windows (windowed correlation) is the desired operation. Fast response is desirable in many applications (e.g., to aim a telescope at an activity of interest or to perform a stock trade). These three factors -- data size, windowed correlation, and fast response -- motivate this work.Previous work [10, 14] showed how to compute Pearson correlation using Fast Fourier Transforms and Wavelet transforms, but such techniques don't work for time series in which the energy is spread over many frequency components, thus resembling white noise. For such "uncooperative" time series, this paper shows how to combine several simple techniques -- sketches (random projections), convolution, structured random vectors, grid structures, and combinatorial design -- to achieve high performance windowed Pearson correlation over a variety of data sets.
Parallel mining of closed sequential patterns
Authors: Shengnan Cong, Jiawei Han, David A. Padua
Venue: KDD
Year: 2005

Discovery of sequential patterns is an essential data mining task with broad applications. Among several variations of sequential patterns, closed sequential pattern is the most useful one since it retains all the information of the complete pattern set but is often much more compact than it. Unfortunately, there is no parallel closed sequential pattern mining method proposed yet. In this paper we develop an algorithm, called Par-CSP (Parallel Closed Sequential Pattern mining), to conduct parallel mining of closed sequential patterns on a distributed memory system. Par-CSP partitions the work among the processors by exploiting the divide-and-conquer property so that the overhead of interprocessor communication is minimized. Par-CSP applies dynamic scheduling to avoid processor idling. Moreover, it employs a technique, called selective sampling to address the load imbalance problem. We implement Par-CSP using MPI on a 64-node Linux cluster. Our experimental results show that Par-CSP attains good parallelization efficiencies on various input datasets.
Mining images on semantics via statistical learning
Authors: Jianping Fan, Hangzai Luo, Mohand-Said Hacid
Venue: KDD
Year: 2005

In this paper, we have proposed a novel framework to enable hierarchical image classification via statistical learning. By integrating the concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level modeling of semantic image concepts and hierarchical classifier combination. Thus, learning the classifiers for the semantic image concepts at the high level of the concept hierarchy can be effectively achieved by detecting the presences of the relevant base-level atomic image concepts. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effective model selection and parameter estimation. In addition, a novel penalty term is proposed to effectively eliminate the misleading effects of the outlying unlabeled images on semi-supervised classifier training. Our experimental results in a specific image domain of outdoor photos are very attractive.
Creating social networks to improve peer-to-peer networking
Authors: Andrew S. Fast, David D. Jensen, Brian Neil Levine
Venue: KDD
Year: 2005

We use knowledge discovery techniques to guide the creation of efficient overlay networks for peer-to-peer file sharing. An overlay network specifies the logical connections among peers in a network and is distinct from the physical connections of the network. It determines the order in which peers will be queried when a user is searching for a specific file. To better understand the role of the network overlay structure in the performance of peer-to-peer file sharing protocols, we compare several methods for creating overlay networks. We analyze the networks using data from a campus network for peer-to-peer file sharing that recorded anonymized data on 6,528 users sharing 291,925 music files over an 81-day period. We propose a novel protocol for overlay creation based on a model of user preference identified by latent-variable clustering with hierarchical Dirichlet processes (HDPs). Our simulations and empirical studies show that the clusters of songs created by HDPs effectively model user behavior and can be used to create desirable network overlays that outperform alternative approaches.
Finding similar files in large document repositories
Authors: George Forman, Kave Eshghi, Stephane Chiocchetti
Venue: KDD
Year: 2005

Hewlett-Packard has many millions of technical support documents in a variety of collections. As part of content management, such collections are periodically merged and groomed. In the process, it becomes important to identify and weed out support documents that are largely duplicates of newer versions. Doing so improves the quality of the collection, eliminates chaff from search results, and improves customer satisfaction.The technical challenge is that through workflow and human processes, the knowledge of which documents are related is often lost. We required a method that could identify similar documents based on their content alone, without relying on metadata, which may be corrupt or missing.We present an approach for finding similar files that scales up to large document repositories. It is based on chunking the byte stream to find unique signatures that may be shared in multiple files. An analysis of the file-chunk graph yields clusters of related files. An optional bipartite graph partitioning algorithm can be applied to greatly increase scalability.
Mining the internet: the eighth wonder of the world
Authors: Gian Fulgoni
Venue: KDD
Year: 2005

The Internet takes behavioral consumer research to a new level by providing the ability to passively and continuously monitor the complete online behavior of millions of consumers in an opt-in, privacy protected manner. Imagine the analytical possibilities if every site visited, every page viewed, content seen, transaction conducted ..... all of this granularity in behavior --- was continuously captured with explicit consumer permission for millions of consumers and privacy was protected. What unique insights could one gain into consumers' behavior, their interests, passions and lifestyles? What behavior could be predicted? What commercial applications would be possible.
Rule extraction from linear support vector machines
Authors: Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
Venue: KDD
Year: 2005

We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike the original classifier, can be easily interpreted by humans. Each iteration of the rule extraction algorithm is formulated as a constrained optimization problem that is computationally inexpensive to solve. We discuss various properties of the algorithm and provide proof of convergence for two different optimization criteria We demonstrate the performance and the speed of the algorithm on linear classifiers learned from real-world datasets, including a medical dataset on detection of lung cancer from medical images. The ability to convert SVM's and other "black-box" classifiers into a set of human-understandable rules, is critical not only for physician acceptance, but also to reducing the regulatory barrier for medical-decision support systems based on such classifiers.
Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering
Authors: Bin Gao, Tie-Yan Liu, Xin Zheng, QianSheng Cheng, Wei-Ying Ma
Venue: KDD
Year: 2005

Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two types of heterogeneous data (denoted by pair-wise co-clustering), such as documents and terms, have been well studied in the literature, the work on more types of heterogeneous data (denoted by high-order co-clustering) is still very limited. As an attempt in this direction, in this paper, we worked on a specific case of high-order co-clustering in which there is a central type of objects that connects the other types so as to form a star structure of the inter-relationships. Actually, this case could be a very good abstract for many real-world applications, such as the co-clustering of categories, documents and terms in text mining. In our philosophy, we treated such kind of problems as the fusion of multiple pair-wise co-clustering sub-problems with the constraint of the star structure. Accordingly, we proposed the concept of consistent bipartite graph co-partitioning, and developed an algorithm based on semi-definite programming (SDP) for efficient computation of the clustering results. Experiments on toy problems and real data both verified the effectiveness of our proposed method.
Dimension induced clustering
Authors: Aristides Gionis, Alexander Hinneburg, Spiros Papadimitriou, Panayiotis Tsaparas
Venue: KDD
Year: 2005

It is commonly assumed that high-dimensional datasets contain points most of which are located in low-dimensional manifolds. Detection of low-dimensional clusters is an extremely useful task for performing operations such as clustering and classification, however, it is a challenging computational problem. In this paper we study the problem of finding subsets of points with low intrinsic dimensionality. Our main contribution is to extend the definition of fractal correlation dimension, which measures average volume growth rate, in order to estimate the intrinsic dimensionality of the data in local neighborhoods. We provide a careful analysis of several key examples in order to demonstrate the properties of our measure. Based on our proposed measure, we introduce a novel approach to discover clusters with low dimensionality. The resulting algorithms extend previous density based measures, which have been successfully used for clustering. We demonstrate the effectiveness of our algorithms for discovering low-dimensional m-flats embedded in high dimensional spaces, and for detecting low-rank sub-matrices.
Mining tree queries in a graph
Authors: Bart Goethals, Eveline Hoekx, Jan Van den Bussche
Venue: KDD
Year: 2005

We present an algorithm for mining tree-shaped patterns in a large graph. Novel about our class of patterns is that they can contain constants, and can contain existential nodes which are not counted when determining the number of occurrences of the pattern in the graph. Our algorithm has a number of provable optimality properties, which are based on the theory of conjunctive database queries. We propose a database-oriented implementation in SQL, and report upon some initial experimental results obtained with our implementation on graph data about food webs, about protein interactions, and about citation analysis.
Non-redundant clustering with conditional ensembles
Authors: David Gondek, Thomas Hofmann
Venue: KDD
Year: 2005

Data may often contain multiple plausible clusterings. In order to discover a clustering which is useful to the user, constrained clustering techniques have been proposed to guide the search. Typically, these techniques assume background knowledge in the form of explicit information about the desired clustering. In contrast, we consider the setting in which the background knowledge is instead about an undesired clustering. Such knowledge may be obtained from an existing classification or precedent algorithm. The problem is then to find a novel, "orthogonal" clustering in the data. We present a general algorithmic framework which makes use of cluster ensemble methods to solve this problem. One key advantage of this approach is that it takes a base clustering method which is used as a black box, allowing the practitioner to select the most appropriate clustering method for the domain. We present experimental results on synthetic and text data which establish the competitiveness of this framework.
Wavelet synopsis for data streams: minimizing non-euclidean error
Authors: Sudipto Guha, Boulos Harb
Venue: KDD
Year: 2005

We consider the wavelet synopsis construction problem for data streams where given n numbers we wish to estimate the data by constructing a synopsis, whose size, say B is much smaller than n. The B numbers are chosen to minimize a suitable error between the original data and the estimate derived from the synopsis.Several good one-pass wavelet construction streaming algorithms minimizing the l2 error exist. For other error measures, the problem is less understood. We provide the first one-pass small space streaming algorithms with provable error guarantees (additive approximation) for minimizing a variety of non-Euclidean error measures including all weighted lp (including l∞) and relative error lp metrics.In several previous works solutions (for weighted l2, l∞ and maximum relative error) where the B synopsis coefficients are restricted to be wavelet coefficients of the data were proposed. This restriction yields suboptimal solutions on even fairly simple examples. Other lines of research, such as probabilistic synopsis, imposed restrictions on how the synopsis was arrived at. To the best of our knowledge this paper is the first paper to address the general problem, without any restriction on how the synopsis is arrived at, as well as provide the first streaming algorithms with guaranteed performance for these classes of error measures.
Unweaving a web of documents
Authors: Ramanathan V. Guha, Ravi Kumar, D. Sivakumar, Ravi Sundaram
Venue: KDD
Year: 2005

We develop an algorithmic framework to decompose a collection of time-stamped text documents into semantically coherent threads. Our formulation leads to a graph decomposition problem on directed acyclic graphs, for which we obtain three algorithms --- an exact algorithm that is based on minimum cost flow and two more efficient algorithms based on maximum matching and dynamic programming that solve specific versions of the graph decomposition problem. Applications of our algorithms include superior summarization of news search results, improved browsing paradigms for large collections of text-intensive corpora, and integration of time-stamped documents from a variety of sources. Experimental results based on over 250,000 news articles from a major newspaper over a period of four years demonstrate that our algorithms efficiently identify robust threads of varying lengths and time-spans.
Making holistic schema matching robust: an ensemble approach
Authors: Bin He, Kevin Chen-Chuan Chang
Venue: KDD
Year: 2005

The has been rapidly deepened by myriad searchable databases online, where data are hidden behind query interfaces. As an essential task toward integrating these massive deep Web sources, large scale schema matching (i.e., discovering semantic correspondences of attributes across many query interfaces) has been actively studied recently. In particular, many works have emerged to address this problem by holistically matching many schemas at the same time and thus pursuing mining approaches in nature. However, while holistic schema matching has built its promise upon the large quantity of input schemas, it also suffers the robustness problem caused by noisy data quality. Such noises often inevitably arise in the automatic extraction of schema data, which is mandatory in large scale integration. For holistic matching to be viable, it is thus essential to make it robust against noisy schemas. To tackle this challenge, we propose a data-ensemble framework with sampling and voting techniques, which is inspired by bagging predictors. Specifically, our approach creates an ensemble of matchers, by randomizing input schema data into many independently downsampled trials, executing the same matcher on each trial and then aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the effectiveness of this data-ensemble framework. Further, empirically, our experiments on real data show that the ensemblization indeed significantly boosts the matching accuracy under noisy schema input, and thus maintains the desired robustness of a holistic matcher.
Maximal boasting
Authors: Cinda Heeren, Leonard Pitt
Venue: KDD
Year: 2005

We introduce the boasting problem, wherein useful trends in historical ordinal data (rankings) are discovered. Claims of the form our object was ranked r or better in x of the last t time units, are formalized, and maximal claims (boasts) of this form are defined under two natural partial orders. For the first partial order, we give an efficient and optimal algorithm for finding all such maximal claims. For the second, we apply a classical result from computational geometry to achieve an algorithm whose running time is significantly more efficient than that of a naive one. Finally, we connect this boasting problem to a novel variation of the problem of finding optimized confidence association rules as originally posed by Fukuda, et al. [2], and give an efficient algorithm for solving a simplification of the new problem.
Combining email models for false positive reduction
Authors: Shlomo Hershkop, Salvatore J. Stolfo
Venue: KDD
Year: 2005

Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolkit, EMT, has been designed to provide a wide range of analyses for arbitrary email sources. Depending upon the task, one can usually achieve very high accuracy, but with some amount of false positive tradeoff. Generally false positives are prohibitively expensive in the real world. In the case of spam detection, for example, even if one email is misclassified, this may be unacceptable if it is a very important email. Much work has been done to improve specific algorithms for the task of detecting unwanted messages, but less work has been report on leveraging multiple algorithms and correlating models in this particular domain of email analysis.EMT has been updated with new correlation functions allowing the analyst to integrate a number of EMT's user behavior models available in the core technology. We present results of combining classifier outputs for improving both accuracy and reducing false positives for the problem of spam detection. We apply these methods to a very large email data set and show results of different combination methods on these corpora. We introduce a new method to compare multiple and combined classifiers, and show how it differs from past work. The method analyzes the relative gain and maximum possible accuracy that can be achieved for certain combinations of classifiers to automatically choose the best combination.