Vector Space Model (VSM) is widely used to represent documents and web pages. It is simple and easy to deal computationally, but it also oversimplifies a document into a vector, susceptible to noise, and cannot explicitly represent underlying topics of a document. A matrix representation of document is proposed in this paper: rows represent distinct terms and columns represent cohesive segments. The matrix model views a document as a set of segments, and each segment is a probability distribution over a limited number of latent topics which can be mapped to clustering structures. The latent topic extraction based on the matrix representation of documents is formulated as a constraint optimization problem in which each matrix (i.e., a document) A_i is factorized into a common base determined by non-negative matrices L and R^top, and a non-negative weight matrix M_i such that the sum of reconstruction error on all documents is minimized. Empirical evaluation demonstrates that it is feasible to use the matrix model for document clustering: (1) compared with vector representation, using matrix representation improves clustering quality consistently, and the proposed approach achieves a relative accuracy improvement up to 66% on the studied datasets, and (2) the proposed method outperforms baseline methods such as k-means and NMF, and complements the state-of-the-art methods like LDA and PLSI. Furthermore, the proposed matrix model allows more refined information retrieval at a segment level instead of at a document level, which enables the return of more relevant documents in information retrieval tasks.
{"title":"Document Clustering via Matrix Representation","authors":"Xufei Wang, Jiliang Tang, Huan Liu","doi":"10.1109/ICDM.2011.59","DOIUrl":"https://doi.org/10.1109/ICDM.2011.59","url":null,"abstract":"Vector Space Model (VSM) is widely used to represent documents and web pages. It is simple and easy to deal computationally, but it also oversimplifies a document into a vector, susceptible to noise, and cannot explicitly represent underlying topics of a document. A matrix representation of document is proposed in this paper: rows represent distinct terms and columns represent cohesive segments. The matrix model views a document as a set of segments, and each segment is a probability distribution over a limited number of latent topics which can be mapped to clustering structures. The latent topic extraction based on the matrix representation of documents is formulated as a constraint optimization problem in which each matrix (i.e., a document) A_i is factorized into a common base determined by non-negative matrices L and R^top, and a non-negative weight matrix M_i such that the sum of reconstruction error on all documents is minimized. Empirical evaluation demonstrates that it is feasible to use the matrix model for document clustering: (1) compared with vector representation, using matrix representation improves clustering quality consistently, and the proposed approach achieves a relative accuracy improvement up to 66% on the studied datasets, and (2) the proposed method outperforms baseline methods such as k-means and NMF, and complements the state-of-the-art methods like LDA and PLSI. Furthermore, the proposed matrix model allows more refined information retrieval at a segment level instead of at a document level, which enables the return of more relevant documents in information retrieval tasks.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133274488","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}
Yexi Jiang, Chang-Shing Perng, Tao Li, Rong N. Chang
The promise of cloud computing is to provide computing resources instantly whenever they are needed. The state-of-art virtual machine (VM) provisioning technology can provision a VM in tens of minutes. This latency is unacceptable for jobs that need to scale out during computation. To truly enable on-the-fly scaling, new VM needs to be ready in seconds upon request. In this paper, We present an online temporal data mining system called ASAP, to model and predict the cloud VM demands. ASAP aims to extract high level characteristics from VM provisioning request stream and notify the provisioning system to prepare VMs in advance. For quantification issue, we propose Cloud Prediction Cost to encodes the cost and constraints of the cloud and guide the training of prediction algorithms. Moreover, we utilize a two-level ensemble method to capture the characteristics of the high transient demands time series. Experimental results using historical data from an IBM cloud in operation demonstrate that ASAP significantly improves the cloud service quality and provides possibility for on-the-fly provisioning.
{"title":"ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning","authors":"Yexi Jiang, Chang-Shing Perng, Tao Li, Rong N. Chang","doi":"10.1109/ICDM.2011.25","DOIUrl":"https://doi.org/10.1109/ICDM.2011.25","url":null,"abstract":"The promise of cloud computing is to provide computing resources instantly whenever they are needed. The state-of-art virtual machine (VM) provisioning technology can provision a VM in tens of minutes. This latency is unacceptable for jobs that need to scale out during computation. To truly enable on-the-fly scaling, new VM needs to be ready in seconds upon request. In this paper, We present an online temporal data mining system called ASAP, to model and predict the cloud VM demands. ASAP aims to extract high level characteristics from VM provisioning request stream and notify the provisioning system to prepare VMs in advance. For quantification issue, we propose Cloud Prediction Cost to encodes the cost and constraints of the cloud and guide the training of prediction algorithms. Moreover, we utilize a two-level ensemble method to capture the characteristics of the high transient demands time series. Experimental results using historical data from an IBM cloud in operation demonstrate that ASAP significantly improves the cloud service quality and provides possibility for on-the-fly provisioning.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126414909","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}
Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, T. D. Bie
Statistical assessment of the results of data mining is increasingly recognised as a core task in the knowledge discovery process. It is of key importance in practice, as results that might seem interesting at first glance can often be explained by well-known basic properties of the data. In pattern mining, for instance, such trivial results can be so overwhelming in number that filtering them out is a necessity in order to identify the truly interesting patterns. In this paper, we propose an approach for assessing results on real-valued rectangular databases. More specifically, using our analytical model we are able to statistically assess whether or not a discovered structure may be the trivial result of the row and column marginal distributions in the database. Our main approach is to use the Maximum Entropy principle to fit a background model to the data while respecting its marginal distributions. To find these distributions, we employ an MDL based histogram estimator, and we fit these in our model using efficient convex optimization techniques. Subsequently, our model can be used to calculate probabilities directly, as well as to efficiently sample data with the purpose of assessing results by means of empirical hypothesis testing. Notably, our approach is efficient, parameter-free, and naturally deals with missing values. As such, it represents a well-founded alternative to swap randomisation
{"title":"Maximum Entropy Modelling for Assessing Results on Real-Valued Data","authors":"Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, T. D. Bie","doi":"10.1109/ICDM.2011.98","DOIUrl":"https://doi.org/10.1109/ICDM.2011.98","url":null,"abstract":"Statistical assessment of the results of data mining is increasingly recognised as a core task in the knowledge discovery process. It is of key importance in practice, as results that might seem interesting at first glance can often be explained by well-known basic properties of the data. In pattern mining, for instance, such trivial results can be so overwhelming in number that filtering them out is a necessity in order to identify the truly interesting patterns. In this paper, we propose an approach for assessing results on real-valued rectangular databases. More specifically, using our analytical model we are able to statistically assess whether or not a discovered structure may be the trivial result of the row and column marginal distributions in the database. Our main approach is to use the Maximum Entropy principle to fit a background model to the data while respecting its marginal distributions. To find these distributions, we employ an MDL based histogram estimator, and we fit these in our model using efficient convex optimization techniques. Subsequently, our model can be used to calculate probabilities directly, as well as to efficiently sample data with the purpose of assessing results by means of empirical hypothesis testing. Notably, our approach is efficient, parameter-free, and naturally deals with missing values. As such, it represents a well-founded alternative to swap randomisation","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126330185","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}
Mining frequent patterns is plagued by the problem of pattern explosion making pattern reduction techniques a key challenge in pattern mining. In this paper we propose a novel theoretical framework for pattern reduction. We do this by measuring the robustness of a property of an item set such as closed ness or non-derivability. The robustness of a property is the probability that this property holds on random subsets of the original data. We study four properties: closed, free, non-derivable and totally shattered item sets, demonstrating how we can compute the robustness analytically without actually sampling the data. Our concept of robustness has many advantages: Unlike statistical approaches for reducing patterns, we do not assume a null hypothesis or any noise model and the patterns reported are simply a subset of all patterns with this property as opposed to approximate patterns for which the property does not really hold. If the underlying property is monotonic, then the measure is also monotonic, allowing us to efficiently mine robust item sets. We further derive a parameter-free technique for ranking item sets that can be used for top-k approaches. Our experiments demonstrate that we can successfully use the robustness measure to reduce the number of patterns and that ranking yields interesting itemsets.
{"title":"Finding Robust Itemsets under Subsampling","authors":"Nikolaj Tatti, Fabian Moerchen","doi":"10.1145/2656261","DOIUrl":"https://doi.org/10.1145/2656261","url":null,"abstract":"Mining frequent patterns is plagued by the problem of pattern explosion making pattern reduction techniques a key challenge in pattern mining. In this paper we propose a novel theoretical framework for pattern reduction. We do this by measuring the robustness of a property of an item set such as closed ness or non-derivability. The robustness of a property is the probability that this property holds on random subsets of the original data. We study four properties: closed, free, non-derivable and totally shattered item sets, demonstrating how we can compute the robustness analytically without actually sampling the data. Our concept of robustness has many advantages: Unlike statistical approaches for reducing patterns, we do not assume a null hypothesis or any noise model and the patterns reported are simply a subset of all patterns with this property as opposed to approximate patterns for which the property does not really hold. If the underlying property is monotonic, then the measure is also monotonic, allowing us to efficiently mine robust item sets. We further derive a parameter-free technique for ranking item sets that can be used for top-k approaches. Our experiments demonstrate that we can successfully use the robustness measure to reduce the number of patterns and that ranking yields interesting itemsets.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124922907","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}
Communities are natural structures observed in social networks and are usually characterized as "relatively dense" subsets of nodes. Social networks change over time and so do the underlying community structures. Thus, to truly uncover this structure we must take the temporal aspect of networks into consideration. Previously, we have represented framework for finding dynamic communities using the social cost model and formulated the corresponding optimization problem [33], assuming that partitions of individuals into groups are given in each time step. We have also presented heuristics and approximation algorithms for the problem, with the same assumption [32]. In general, however, dynamic social networks are represented as a sequence of graphs of snapshots of the social network and the assumption that we have partitions of individuals into groups does not hold. In this paper, we extend the social cost model and formulate an optimization problem of finding community structure from the sequence of arbitrary graphs. We propose a semi definite programming formulation and a heuristic rounding scheme. We show, using synthetic data sets, that this method is quite accurate on synthetic data sets and present its results on a real social network.
{"title":"Finding Communities in Dynamic Social Networks","authors":"Chayant Tantipathananandh, T. Berger-Wolf","doi":"10.1109/ICDM.2011.67","DOIUrl":"https://doi.org/10.1109/ICDM.2011.67","url":null,"abstract":"Communities are natural structures observed in social networks and are usually characterized as \"relatively dense\" subsets of nodes. Social networks change over time and so do the underlying community structures. Thus, to truly uncover this structure we must take the temporal aspect of networks into consideration. Previously, we have represented framework for finding dynamic communities using the social cost model and formulated the corresponding optimization problem [33], assuming that partitions of individuals into groups are given in each time step. We have also presented heuristics and approximation algorithms for the problem, with the same assumption [32]. In general, however, dynamic social networks are represented as a sequence of graphs of snapshots of the social network and the assumption that we have partitions of individuals into groups does not hold. In this paper, we extend the social cost model and formulate an optimization problem of finding community structure from the sequence of arbitrary graphs. We propose a semi definite programming formulation and a heuristic rounding scheme. We show, using synthetic data sets, that this method is quite accurate on synthetic data sets and present its results on a real social network.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125409889","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}
The spectrum of graph has been widely used in graph mining to extract graph topological information. It has also been employed as a characteristic of graph to check the sub graph isomorphism testing since it is an invariant of a graph. However, the spectrum cannot be directly applied to a graph and its sub graph, which is a bottleneck for sub graph isomorphism testing. In this paper, we study the Laplacian spectra between a graph and its sub graph, and propose a method by straightforward adoption of them for sub graph queries. In our proposed method, we first encode every vertex and graph by extracting their Laplacian spectra, and generate a novel two-step filtering conditions. Then, we follow the filtering-and verification framework to conduct sub graph queries. Extensive experiments show that, compared with existing counterpart method, as a graph feature, Laplacian spectra can be used to efficiently improves the efficiency of sub graph queries and thus indicate that it have considerable potential.
{"title":"A Study of Laplacian Spectra of Graph for Subgraph Queries","authors":"Lei Zhu, Qinbao Song","doi":"10.1109/ICDM.2011.17","DOIUrl":"https://doi.org/10.1109/ICDM.2011.17","url":null,"abstract":"The spectrum of graph has been widely used in graph mining to extract graph topological information. It has also been employed as a characteristic of graph to check the sub graph isomorphism testing since it is an invariant of a graph. However, the spectrum cannot be directly applied to a graph and its sub graph, which is a bottleneck for sub graph isomorphism testing. In this paper, we study the Laplacian spectra between a graph and its sub graph, and propose a method by straightforward adoption of them for sub graph queries. In our proposed method, we first encode every vertex and graph by extracting their Laplacian spectra, and generate a novel two-step filtering conditions. Then, we follow the filtering-and verification framework to conduct sub graph queries. Extensive experiments show that, compared with existing counterpart method, as a graph feature, Laplacian spectra can be used to efficiently improves the efficiency of sub graph queries and thus indicate that it have considerable potential.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064074","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}
Multi-instance learning, like other machine learning and data mining tasks, requires distance metrics. Although metric learning methods have been studied for many years, metric learners for multi-instance learning remain almost untouched. In this paper, we propose a framework called Multi-Instance MEtric Learning (MIMEL) to learn an appropriate distance under the multi-instance setting. The distance metric between two bags is defined using the Mahalanobis distance function. The problem is formulated by minimizing the KL divergence between two multivariate Gaussians under the constraints of maximizing the between-class bag distance and minimizing the within-class bag distance. To exploit the mechanism of how instances determine bag labels in multi-instance learning, we design a nonparametric density-estimation-based weighting scheme to assign higher “weights†to the instances that are more likely to be positive in positive bags. The weighting scheme itself has a small workload, which adds little extra computing costs to the proposed framework. Moreover, to further boost the classification accuracy, a kernel version of MIMEL is presented. We evaluate MIMEL, using not only several typical multi-instance tasks, but also two activity recognition datasets. The experimental results demonstrate that MIMEL achieves better classification accuracy than many state-of-the-art distance based algorithms or kernel methods for multi-instance learning.
{"title":"Multi-instance Metric Learning","authors":"Ye Xu, Wei Ping, A. Campbell","doi":"10.1109/ICDM.2011.106","DOIUrl":"https://doi.org/10.1109/ICDM.2011.106","url":null,"abstract":"Multi-instance learning, like other machine learning and data mining tasks, requires distance metrics. Although metric learning methods have been studied for many years, metric learners for multi-instance learning remain almost untouched. In this paper, we propose a framework called Multi-Instance MEtric Learning (MIMEL) to learn an appropriate distance under the multi-instance setting. The distance metric between two bags is defined using the Mahalanobis distance function. The problem is formulated by minimizing the KL divergence between two multivariate Gaussians under the constraints of maximizing the between-class bag distance and minimizing the within-class bag distance. To exploit the mechanism of how instances determine bag labels in multi-instance learning, we design a nonparametric density-estimation-based weighting scheme to assign higher “weights†to the instances that are more likely to be positive in positive bags. The weighting scheme itself has a small workload, which adds little extra computing costs to the proposed framework. Moreover, to further boost the classification accuracy, a kernel version of MIMEL is presented. We evaluate MIMEL, using not only several typical multi-instance tasks, but also two activity recognition datasets. The experimental results demonstrate that MIMEL achieves better classification accuracy than many state-of-the-art distance based algorithms or kernel methods for multi-instance learning.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124860881","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}
Complex systems have been widely studied to characterize their structural behaviors from a topological perspective. High modularity is one of the recurrent features of real-world complex systems. Various graph clustering algorithms have been applied to identifying communities in social networks or modules in biological networks. However, their applicability to real-world systems has been limited because of the massive scale and complex connectivity of the networks. In this study, we exploit a novel information-theoretic model for graph clustering. The entropy-based clustering approach finds locally optimal clusters by growing a random seed in a manner that minimizes graph entropy. We design and analyze modifications that further improve its performance. Assigning priority in seed-selection and seed-growth is well applicable to the scale-free networks characterized by the hub-oriented structure. Computing seed-growth in parallel streams also decomposes an extremely large network efficiently. The experimental results with real biological and social networks show that the entropy-based approach has better performance than competing methods in terms of accuracy and efficiency.
{"title":"Entropy-Based Graph Clustering: Application to Biological and Social Networks","authors":"Edward Casey Kenley, Young-Rae Cho","doi":"10.1109/ICDM.2011.64","DOIUrl":"https://doi.org/10.1109/ICDM.2011.64","url":null,"abstract":"Complex systems have been widely studied to characterize their structural behaviors from a topological perspective. High modularity is one of the recurrent features of real-world complex systems. Various graph clustering algorithms have been applied to identifying communities in social networks or modules in biological networks. However, their applicability to real-world systems has been limited because of the massive scale and complex connectivity of the networks. In this study, we exploit a novel information-theoretic model for graph clustering. The entropy-based clustering approach finds locally optimal clusters by growing a random seed in a manner that minimizes graph entropy. We design and analyze modifications that further improve its performance. Assigning priority in seed-selection and seed-growth is well applicable to the scale-free networks characterized by the hub-oriented structure. Computing seed-growth in parallel streams also decomposes an extremely large network efficiently. The experimental results with real biological and social networks show that the entropy-based approach has better performance than competing methods in terms of accuracy and efficiency.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125268842","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}
Diagnostic genes refer to the genes closely related to a specific disease phenotype, the powers of which to distinguish between different classes are often high. Most methods to discovering the powerful diagnostic genes are either singleton discriminability-based or combination discriminability-based. However, both ignore the abundant interactions among genes, which widely exist in the real world. In this paper, we tackle the problem from a new point of view and make the following contributions: (1) we propose an EWave model, which profitably exploits the ordered expressions among genes based on the defined equivalent dimension group sequences taking into account the "noise" universal in the real data, (2) we devise a novel sequence rule, namely interesting non-redundant contrast sequence rule, which is able to capture the difference between different phenotypes in a high accuracy using as few as possible genes, (3) we present an efficient algorithm called NRMINER to find such rules. Unlike the conventional column enumeration and the more recent row enumeration, it performs a novel template-driven enumeration by making use of the special characteristic of micro array data modeled by EWave. Extensive experiments conducted on various synthetic and real datasets show that: (1) NRMINER is significantly faster than the competing algorithm by up to about one order of magnitude, (2) it provides a higher accuracy using fewer genes. Many diagnostic genes discovered by NRMINER are proved biologically related to some disease.
{"title":"Finding Novel Diagnostic Gene Patterns Based on Interesting Non-redundant Contrast Sequence Rules","authors":"Yuhai Zhao, Guoren Wang, Yuan Li, Zhanghui Wang","doi":"10.1109/ICDM.2011.68","DOIUrl":"https://doi.org/10.1109/ICDM.2011.68","url":null,"abstract":"Diagnostic genes refer to the genes closely related to a specific disease phenotype, the powers of which to distinguish between different classes are often high. Most methods to discovering the powerful diagnostic genes are either singleton discriminability-based or combination discriminability-based. However, both ignore the abundant interactions among genes, which widely exist in the real world. In this paper, we tackle the problem from a new point of view and make the following contributions: (1) we propose an EWave model, which profitably exploits the ordered expressions among genes based on the defined equivalent dimension group sequences taking into account the \"noise\" universal in the real data, (2) we devise a novel sequence rule, namely interesting non-redundant contrast sequence rule, which is able to capture the difference between different phenotypes in a high accuracy using as few as possible genes, (3) we present an efficient algorithm called NRMINER to find such rules. Unlike the conventional column enumeration and the more recent row enumeration, it performs a novel template-driven enumeration by making use of the special characteristic of micro array data modeled by EWave. Extensive experiments conducted on various synthetic and real datasets show that: (1) NRMINER is significantly faster than the competing algorithm by up to about one order of magnitude, (2) it provides a higher accuracy using fewer genes. Many diagnostic genes discovered by NRMINER are proved biologically related to some disease.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130098483","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}
In this paper, we propose a new variant of supervised Latent Dirichlet Allocation(sLDA): mixture of soft max sLDA, for image classification. Ensemble classification methods can combine multiple weak classifiers to construct a strong classifier. Inspired by the ensemble idea, we try to improve sLDA model using the idea. The mixture of soft max model is a probabilistic ensemble classification model, it can fit the training data and class label well. We embed the mixture of soft max model into LDA model under the framwork of sLDA, and construct an ensemble supervised topic model for image classification. Meanwhile, we derive an elegant parameters estimation algorithm based on variational EM method, and give a simple and efficient approximation method for classifying a new image. Finally, we demonstrate the effectiveness of our model by comparing with some existing approaches on two real world datasets. The results show that our model enhances classification accuracy by 7% on the 1600-image Label Me dataset and 9% on the 1791-image UIUC-Sport dataset.
{"title":"Mixture of Softmax sLDA","authors":"Xiaoxu Li, Junyu Zeng, Xiaojie Wang, Yixin Zhong","doi":"10.1109/ICDM.2011.103","DOIUrl":"https://doi.org/10.1109/ICDM.2011.103","url":null,"abstract":"In this paper, we propose a new variant of supervised Latent Dirichlet Allocation(sLDA): mixture of soft max sLDA, for image classification. Ensemble classification methods can combine multiple weak classifiers to construct a strong classifier. Inspired by the ensemble idea, we try to improve sLDA model using the idea. The mixture of soft max model is a probabilistic ensemble classification model, it can fit the training data and class label well. We embed the mixture of soft max model into LDA model under the framwork of sLDA, and construct an ensemble supervised topic model for image classification. Meanwhile, we derive an elegant parameters estimation algorithm based on variational EM method, and give a simple and efficient approximation method for classifying a new image. Finally, we demonstrate the effectiveness of our model by comparing with some existing approaches on two real world datasets. The results show that our model enhances classification accuracy by 7% on the 1600-image Label Me dataset and 9% on the 1791-image UIUC-Sport dataset.","PeriodicalId":106216,"journal":{"name":"2011 IEEE 11th International Conference on Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117183542","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}
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