2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第2页

Clustering categorical data: A stability analysis framework 聚类分类数据:一个稳定性分析框架

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949452

I. Jarman, T. Etchells, P. Lisboa, Charlene Beynon, J. Martín-Guerrero

{"title":"Clustering categorical data: A stability analysis framework","authors":"I. Jarman, T. Etchells, P. Lisboa, Charlene Beynon, J. Martín-Guerrero","doi":"10.1109/CIDM.2011.5949452","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949452","url":null,"abstract":"Clustering to identify inherent structure is an important first step in data exploration. The k-means algorithm is a popular choice, but K-means is not generally appropriate for categorical data. A specific extension of k-means for categorical data is the k-modes algorithm. Both of these partition clustering methods are sensitive to the initialization of prototypes, which creates the difficulty of selecting the best solution for a given problem. In addition, selecting the number of clusters can be an issue. Further, the k-modes method is especially prone to instability when presented with ‘noisy’ data, since the calculation of the mode lacks the smoothing effect inherent in the calculation of the mean. This is often the case with real-world datasets, for instance in the domain of Public Health, resulting in solutions that can be radically different depending on the initialization and therefore lead to different interpretations. This paper presents two methodologies. The first addresses sensitivity to initializations using a generic landscape mapping of k-mode solutions. The second methodology utilizes the landscape map to stabilize the partition clusters for discrete data, by drawing a consensus sample in order to separate signal from noise components. Results are presented for the benchmark soybean disease dataset, an artificially generated dataset and a case study involving Public Health data.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127129739","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: 3

KB-CB-N classification: Towards unsupervised approach for supervised learning KB-CB-N分类:面向监督学习的无监督方法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949435

Z. Abdallah, M. Gaber

{"title":"KB-CB-N classification: Towards unsupervised approach for supervised learning","authors":"Z. Abdallah, M. Gaber","doi":"10.1109/CIDM.2011.5949435","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949435","url":null,"abstract":"Data classification has attracted considerable research attention in the field of computational statistics and data mining due to its wide range of applications. K Best Cluster Based Neighbour (KB-CB-N) is our novel classification technique based on the integration of three different similarity measures for cluster based classification. The basic principle is to apply unsupervised learning on the instances of each class in the dataset and then use the output as an input for the classification algorithm to find the K best neighbours of clusters from the density, gravity and distance perspectives. Clustering is applied as an initial step within each class to find the inherent in-class grouping in the dataset. Different data clustering techniques use different similarity measures. Each measure has its own strength and weakness. Thus, combining the three measures can benefit from the strength of each one and eliminate encountered problems of using an individual measure. Extensive experimental results using eight real datasets have evidenced that our new technique typically shows improved or equivalent performance over other existing state-of-the-art classification methods.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133416846","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: 12

Online autoregressive prediction in time series with delayed disclosure 时滞披露时间序列的在线自回归预测

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949440

J. Andreoli, Marie-Luise Schneider

引用次数: 0

Partially supervised k-harmonic means clustering 部分监督k调和均值聚类

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949424

T. Runkler

引用次数: 10

Increased classification accuracy and speedup through pair-wise feature selection for support vector machines 通过对支持向量机的成对特征选择，提高了分类精度和速度

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949457

K. Kramer, Dmitry Goldgof, L. Hall, A. Remsen

{"title":"Increased classification accuracy and speedup through pair-wise feature selection for support vector machines","authors":"K. Kramer, Dmitry Goldgof, L. Hall, A. Remsen","doi":"10.1109/CIDM.2011.5949457","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949457","url":null,"abstract":"Support vector machines are binary classifiers that can implement multi-class classifiers by creating a classifier for each possible combination of classes or for each class using a one class versus all strategy. Feature selection algorithms often search for a single set of features to be used by each of the binary classifiers. This ignores the fact that features that may be good discriminators for two particular classes might not do well for other class combinations. As a result, the feature selection process may not include these features in the common set to be used by all support vector machines. It is shown that by selecting features for each binary class combination, overall classification accuracy can be improved (as much as 2.1%), feature selection time can be significantly reduced (speed up of 3.2 times), and time required for training a multi-class support vector machine is reduced. Another benefit of this approach is that considerably less time is required for feature selection when additional classes are added to the training data. This is because the features selected for the existing class combinations are still valid, so that feature selection only needs to be run for the new class combinations created.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129496053","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: 7

Partial generalized correlation for hyperspectral data 高光谱数据的部分广义相关

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949422

M. Strickert, B. Labitzke, V. Blanz

引用次数: 1

Periodic quick test for classifying long-term activities 对长期活动进行分类的定期快速测试

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949426

Pekka Siirtola, Heli Koskimäki, J. Röning

{"title":"Periodic quick test for classifying long-term activities","authors":"Pekka Siirtola, Heli Koskimäki, J. Röning","doi":"10.1109/CIDM.2011.5949426","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949426","url":null,"abstract":"A novel method to classify long-term human activities is presented in this study. The method consists of two parts: quick test and periodic classification. The quick test uses temporal information to improve recognition accuracy, while the periodic classification is based on the assumption that recognized activities are long-term. Periodic quick test (PQT) classification was tested using a data set consisting of six long-term sports exercises. The data were collected from six persons wearing a two-dimensional accelerometer on their wrist. The results show that the presented method is not only faster than a normal method, that does not use temporal information and does not assume that activities are long-term, but also more accurate. The results were compared with a normal sliding window technique which divides signal into smaller sequences and classifies each sequence into one of the six classes. The classification accuracy using a normal method was around 84% while using PQT the recognition rate was over 90%. In addition, the number of classified sequences using a normal method was over six times higher than using PQT.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116384545","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: 11

FGMAC: Frequent subgraph mining with Arc Consistency 基于弧一致性的频繁子图挖掘

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949436

Brahim Douar, M. Liquiere, C. Latiri, Y. Slimani

引用次数: 5

Multiple query-dependent RankSVM aggregation for document retrieval 用于文档检索的多查询依赖的RankSVM聚合

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949420

Yang Wang, Min Lu, X. Pang, Maoqiang Xie, Yalou Huang

引用次数: 0

A GPU-based interactive bio-inspired visual clustering 基于gpu的交互式生物视觉聚类

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949300

U. Erra, Bernardino Frola, V. Scarano

引用次数: 2