Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining最新文献_第3页

Learning incoherent sparse and low-rank patterns from multiple tasks 从多个任务中学习不连贯的稀疏和低秩模式

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835952

Jianhui Chen, Ji Liu, Jieping Ye

{"title":"Learning incoherent sparse and low-rank patterns from multiple tasks","authors":"Jianhui Chen, Ji Liu, Jieping Ye","doi":"10.1145/1835804.1835952","DOIUrl":"https://doi.org/10.1145/1835804.1835952","url":null,"abstract":"We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. In addition, we present two projected gradient algorithms and discuss their rates of convergence. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81762746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 71

Probably the best itemsets 可能是最好的道具集

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835843

Nikolaj Tatti

{"title":"Probably the best itemsets","authors":"Nikolaj Tatti","doi":"10.1145/1835804.1835843","DOIUrl":"https://doi.org/10.1145/1835804.1835843","url":null,"abstract":"One of the main current challenges in itemset mining is to discover a small set of high-quality itemsets. In this paper we propose a new and general approach for measuring the quality of itemsets. The method is solidly founded in Bayesian statistics and decreases monotonically, allowing for efficient discovery of all interesting itemsets. The measure is defined by connecting statistical models and collections of itemsets. This allows us to score individual itemsets with the probability of them occuring in random models built on the data. As a concrete example of this framework we use exponential models. This class of models possesses many desirable properties. Most importantly, Occam's razor in Bayesian model selection provides a defence for the pattern explosion. As general exponential models are infeasible in practice, we use decomposable models; a large sub-class for which the measure is solvable. For the actual computation of the score we sample models from the posterior distribution using an MCMC approach. Experimentation on our method demonstrates the measure works in practice and results in interpretable and insightful itemsets for both synthetic and real-world data.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82647434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Scalable similarity search with optimized kernel hashing 可扩展的相似性搜索与优化的内核哈希

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835946

Junfeng He, W. Liu, Shih-Fu Chang

{"title":"Scalable similarity search with optimized kernel hashing","authors":"Junfeng He, W. Liu, Shih-Fu Chang","doi":"10.1145/1835804.1835946","DOIUrl":"https://doi.org/10.1145/1835804.1835946","url":null,"abstract":"Scalable similarity search is the core of many large scale learning or data mining applications. Recently, many research results demonstrate that one promising approach is creating compact and efficient hash codes that preserve data similarity. By efficient, we refer to the low correlation (and thus low redundancy) among generated codes. However, most existing hash methods are designed only for vector data. In this paper, we develop a new hashing algorithm to create efficient codes for large scale data of general formats with any kernel function, including kernels on vectors, graphs, sequences, sets and so on. Starting with the idea analogous to spectral hashing, novel formulations and solutions are proposed such that a kernel based hash function can be explicitly represented and optimized, and directly applied to compute compact hash codes for new samples of general formats. Moreover, we incorporate efficient techniques, such as Nystrom approximation, to further reduce time and space complexity for indexing and search, making our algorithm scalable to huge data sets. Another important advantage of our method is the ability to handle diverse types of similarities according to actual task requirements, including both feature similarities and semantic similarities like label consistency. We evaluate our method using both vector and non-vector data sets at a large scale up to 1 million samples. Our comprehensive results show the proposed method outperforms several state-of-the-art approaches for all the tasks, with a significant gain for most tasks.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80307372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 147

Universal multi-dimensional scaling 通用多维标度

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835948

Arvind Agarwal, J. M. Phillips, Suresh Venkatasubramanian

引用次数: 32

Data mining to predict and prevent errors in health insurance claims processing 用于预测和防止健康保险索赔处理中的错误的数据挖掘

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835816

Mohit Kumar, R. Ghani, Z. Mei

{"title":"Data mining to predict and prevent errors in health insurance claims processing","authors":"Mohit Kumar, R. Ghani, Z. Mei","doi":"10.1145/1835804.1835816","DOIUrl":"https://doi.org/10.1145/1835804.1835816","url":null,"abstract":"Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make the system easily deployable across health insurance companies.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78634899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 69

Fast nearest-neighbor search in disk-resident graphs 磁盘驻留图中的快速最近邻搜索

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835871

Purnamrita Sarkar, A. Moore

{"title":"Fast nearest-neighbor search in disk-resident graphs","authors":"Purnamrita Sarkar, A. Moore","doi":"10.1145/1835804.1835871","DOIUrl":"https://doi.org/10.1145/1835804.1835871","url":null,"abstract":"Link prediction, personalized graph search, fraud detection, and many such graph mining problems revolve around the computation of the most \"similar\" k nodes to a given query node. One widely used class of similarity measures is based on random walks on graphs, e.g., personalized pagerank, hitting and commute times, and simrank. There are two fundamental problems associated with these measures. First, existing online algorithms typically examine the local neighborhood of the query node which can become significantly slower whenever high-degree nodes are encountered (a common phenomenon in real-world graphs). We prove that turning high degree nodes into sinks results in only a small approximation error, while greatly improving running times. The second problem is that of computing similarities at query time when the graph is too large to be memory-resident. The obvious solution is to split the graph into clusters of nodes and store each cluster on a disk page; ideally random walks will rarely cross cluster boundaries and cause page-faults. Our contributions here are twofold: (a) we present an efficient deterministic algorithm to find the k closest neighbors (in terms of personalized pagerank) of any query node in such a clustered graph, and (b) we develop a clustering algorithm (RWDISK) that uses only sequential sweeps over data files. Empirical results on several large publicly available graphs like DBLP, Citeseer and Live-Journal (~ 90 M edges) demonstrate that turning high degree nodes into sinks not only improves running time of RWDISK by a factor of 3 but also boosts link prediction accuracy by a factor of 4 on average. We also show that RWDISK returns more desirable (high conductance and small size) clusters than the popular clustering algorithm METIS, while requiring much less memory. Finally our deterministic algorithm for computing nearest neighbors incurs far fewer page-faults (factor of 5) than actually simulating random walks.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82844346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 58

Generative models for ticket resolution in expert networks 专家网络中票据解析的生成模型

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835897

Gengxin Miao, L. Moser, Xifeng Yan, S. Tao, Yi Chen, Nikos Anerousis

{"title":"Generative models for ticket resolution in expert networks","authors":"Gengxin Miao, L. Moser, Xifeng Yan, S. Tao, Yi Chen, Nikos Anerousis","doi":"10.1145/1835804.1835897","DOIUrl":"https://doi.org/10.1145/1835804.1835897","url":null,"abstract":"Ticket resolution is a critical, yet challenging, aspect of the delivery of IT services. A large service provider needs to handle, on a daily basis, thousands of tickets that report various types of problems. Many of those tickets bounce among multiple expert groups before being transferred to the group with the right expertise to solve the problem. Finding a methodology that reduces such bouncing and hence shortens ticket resolution time is a long-standing challenge. In this paper, we present a unified generative model, the Optimized Network Model (ONM), that characterizes the lifecycle of a ticket, using both the content and the routing sequence of the ticket. ONM uses maximum likelihood estimation, to represent how the information contained in a ticket is used by human experts to make ticket routing decisions. Based on ONM, we develop a probabilistic algorithm to generate ticket routing recommendations for new tickets in a network of expert groups. Our algorithm calculates all possible routes to potential resolvers and makes globally optimal recommendations, in contrast to existing classification methods that make static and locally optimal recommendations. Experiments show that our method significantly outperforms existing solutions.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85944810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 48

Negative correlations in collaboration: concepts and algorithms 协作中的负相关:概念和算法

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835864

Jinyan Li, Qian Liu, Tao Zeng

引用次数: 7

Transfer metric learning by learning task relationships 通过学习任务关系迁移度量学习

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835954

Yu Zhang, D. Yeung

{"title":"Transfer metric learning by learning task relationships","authors":"Yu Zhang, D. Yeung","doi":"10.1145/1835804.1835954","DOIUrl":"https://doi.org/10.1145/1835804.1835954","url":null,"abstract":"Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric. However, the labeled data available in many applications is scarce and hence the metrics learned are often unsatisfactory. In this paper, we consider a transfer learning setting in which some related source tasks with labeled data are available to help the learning of the target task. We first propose a convex formulation for multi-task metric learning by modeling the task relationships in the form of a task covariance matrix. Then we regard transfer learning as a special case of multi-task learning and adapt the formulation of multi-task metric learning to the transfer learning setting for our method, called transfer metric learning (TML). In TML, we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem has an efficient solution. Experimental results on some commonly used transfer learning applications demonstrate the effectiveness of our method.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89379051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 102

Finding effectors in social networks 在社交网络中寻找效应

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835937

Theodoros Lappas, Evimaria Terzi, D. Gunopulos, H. Mannila

引用次数: 216