一种新的改进聚类精确度和稳定性的融合技术
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摘要
数据分组是理解学习中的一个基本和重要的模式,相似的模型被聚集到同一个分组中,不同的模型在不同的分组中。本论文提出了一种基于统计共识的聚类融合算法来提高聚类的准确性和稳定性,算法可以应用于隐私问题数据或大规模不能聚集到一个位置的分布式数据挖掘中。融合方法已广泛应用于监督式学习,并且事实证明,融合方法比单一的预测＼学习模式相比,能将预测误差减少到相当高的程度。近些年,人们正在研究非监督的学习(聚类融合),期望获得可喜成果。本文所提出的基于统计共识的聚类融合方法通过四个步骤获得最后的共识聚类结果。
第一步是利用K-means算法在不同的初始参数下运行多次产生聚类成员。初始参数对K-means算法的影响是比较大的,采用不同的初始参数在同一数据集上得到多种聚类结果。第二步是在产生聚类成员中选择一个最佳聚类。这部分通过基于K- means算法定义的目标函数来实现,这个目标函数可以减小误差并使得类之间的紧凑度和分离度更好。由于缺少标记,误差是能判断聚类分析质量的很好的数学方法。第三步是融合方法,论文采用选择性聚类融合方法,选取一致性聚类并丢弃不一致的聚类。在融合中,利用信息理论(互信息)作为选择一致性聚类的标准,第四步是一致性函数。最后的聚类结果是利用一致性聚类成员使用统计共识函数得到的。
论文所研究聚类融合算法改善了聚类结果的精确性以及稳定性。由于聚类融合在数据挖掘和机器学习中有很大影响力,将多种聚类模型融合到一种聚类方法中,通常效果会比单一聚类算法好。大多数数据挖掘和知识发现技术是针对建模,而不是在结果的精确度上。但是对于复杂商业智能系统,确实需要更多关注聚类精确度,而不是聚类建模。任何商务智能系统都需要一个高质量聚类作为其核心,在大多数情况下它涉及到大量数据,并且数据有时可能在分布式环境下。问题在于,现有的经典聚类算法并不稳定,它们的不稳定导致在不准确的聚类结果,同时因为经典聚类算法假设数据是在单一的位置上,所以这些算法并不适合数据不能合并到单一位置的分布式数据环境。
本文提出的新聚类融合算法除了提高稳定性和聚类结果精确度外,它还可以用于分布式数据的聚类。分布式数据挖掘是数据挖掘的有趣的方面之一,尤其是当数据集因存储(通常数据挖掘涉及到大量的数据)或隐私性等原因,不能合并到一个位置。单一的经典聚类算法是不能处理这些情况的。我们的方法使用许多模式和聚类中心表示聚类,这使得我们的算法独特于现有的使用类标签标识每个模式或数据点的聚类融合方法。用聚类中心和大量模式表示的聚类,直接解决了标签对应问题,而不用像现有的大多数算法引用额外的技术。这种方法也节省了时间与空间,共识函数只需要聚类中心和数据点数量这些信息,它远远小于数据集中的实际数据点数量,这使得我们的算法适用于处理并行或分布式环境中的大量数据。实验结果表明,本文提出的聚类融合算法与k-means经典聚类算反比较,算法的精度性和稳定性更好。
论文章节安排如下：第一章介绍了数据挖掘和知识发现涉及的技术理念以及其应用。第二章着重于聚类和聚类融合,并对现有融合算法及技术进行了综述,第三章是提出的新聚类融合算法。第四章是实验与评估,第五章是结论。最后是感谢,参考文献和附录。
Organizing Data into sensible grouping is one of the most fundamental and crucial modes of understanding learning where by similar patterns are grouped together in one group and those not similar into another groups. This thesis presents a Statistical Consensus method for Cluster ensemble to improve clustering accuracy and stability and in some situations it can serve in distributed data mining in either case whether privacy issues or massive volume of data that cannot be pooled into a single location for processing or both. Ensemble methods have been popularly in supervised learning whereby it proved to reduce predictive errors to a considerable high margin compared to a single best classical predictor/learning model. Likewise recently intensive researches are in progress working on unsupervised learning (cluster ensembles) in which promising results are attained. Our proposed Clustering Ensemble technique comprises of four sub-parts on the way through the process in attaining final consensus clustering result.
The first part of our approach is generating the partitions in which K-means Clustering algorithm is employed with different initiations and run the algorithm several times. K-means algorithm is sensitive to initial parameters whereby different initialization leads to diverse clustering results from same dataset. The second part is the selection of a single best clustering among generated partitions. This is achieved by employing the objective function from the k-means clustering algorithm which is regarded as an error in this case. The k-means clustering states that, the minimum this error implies compact and well separated clusters which is the essence to clustering. Due to the lack of labels, the error is the only well proved mathematical measure in clustering quality analysis. The third part of the consensus method is the selection of consistent clusterings in which the inconsistency partitions or clustering are filtered out from the ensemble, only the consistency partitions/clustering are included in the consensus process. Here the information theory; Mutual Information (MI) is employed as criteria in selecting consistent partitions and the fourth part of our method is the consensus function. The final clustering result is achieved by fusing/combining the consistency partitions in the ensemble with a Statistical Consensus function.
Our research is based on Cluster Ensembles in which the concentration and focus is at improving the Accuracy of clustering result including Stability. Due to being the most influential development technique in Data Mining and Machine Learning, ensemble techniques combine multiple models into one which is usually more quality than the best of its components. Most Data Mining and Knowledge Discovery techniques emphasize more effort on model building rather than accuracy for stance in marketing, intrusion detections in networks and alike. But on contrary complex business intelligent systems like auditing, fraud detections including criminal detections do require much attention on clustering accuracy rather than the model. Any business intelligent system needs a quality clustering as its core technique and in most cases it involves huge volumes of data which sometimes can be in distributed environment. The issue is that, available classical clustering algorithms are not stable. Their instability leads to inaccurate clustering results at the same time they are not suitable for applications in distributed data environments whereby data cannot be pooled into a single location for processing because classical clustering techniques assumes that the data is located at single location.
The proposed new Consensus approach for Cluster Ensembles apart from producing stable and accurate clustering results, it offers capability for clustering distributed data sets. Distributed data mining is one of the interesting aspects in data mining especially when dataset cannot be pooled into a single location due to either storage issues (always data mining involve massive data) or privacy reasons. As prescribed before from previous paragraph, in these kinds of situations single classic clustering technique cannot handle. Our Consensus method uses number of patterns and cluster centers as representation for the clusters; this makes our technique to be unique from existing cluster ensemble methods in which they tend to use labels from each pattern or data point. The representation of clusters with cluster centers and its number of patterns resolves the label correspondence problem directly without introducing additional technique which is always the case in most existing cluster ensemble methods. This technique also serves time and storage since the only information needed for the consensus is the cluster's centers with their number of data points which is always much less that the actual number of data points in the dataset, this makes our consensus suitable for clustering massive volume of data in parallel and or distributed environment. Experiment results from real datasets shows that, our New Consensus clustering produces improved result in terms of accuracy and stability compared to its components which is classical k-means clustering algorithm in this case and can deal with massive volume of data in distributed environment.
The Thesis is presented as follows:The first chapter introduces the idea of Data Mining and Knowledge Discovery, techniques involved, applications including the challenges involves with a literature review. The second chapter focuses on the Ensembles and Clustering Ensembles reviewing the technique and existing ensemble methods citing their strengths and weaknesses, while the third chapter is presents the proposed Ensemble technique. The fourth chapter is about the Experiments undertaken and the Evaluation of results, with the fifth chapter giving the conclusion. The last three sections are about the Autography, the Appendices and the Acknowledgements respectively.
第一步是利用K-means算法在不同的初始参数下运行多次产生聚类成员。初始参数对K-means算法的影响是比较大的,采用不同的初始参数在同一数据集上得到多种聚类结果。第二步是在产生聚类成员中选择一个最佳聚类。这部分通过基于K- means算法定义的目标函数来实现,这个目标函数可以减小误差并使得类之间的紧凑度和分离度更好。由于缺少标记,误差是能判断聚类分析质量的很好的数学方法。第三步是融合方法,论文采用选择性聚类融合方法,选取一致性聚类并丢弃不一致的聚类。在融合中,利用信息理论(互信息)作为选择一致性聚类的标准,第四步是一致性函数。最后的聚类结果是利用一致性聚类成员使用统计共识函数得到的。
论文所研究聚类融合算法改善了聚类结果的精确性以及稳定性。由于聚类融合在数据挖掘和机器学习中有很大影响力,将多种聚类模型融合到一种聚类方法中,通常效果会比单一聚类算法好。大多数数据挖掘和知识发现技术是针对建模,而不是在结果的精确度上。但是对于复杂商业智能系统,确实需要更多关注聚类精确度,而不是聚类建模。任何商务智能系统都需要一个高质量聚类作为其核心,在大多数情况下它涉及到大量数据,并且数据有时可能在分布式环境下。问题在于,现有的经典聚类算法并不稳定,它们的不稳定导致在不准确的聚类结果,同时因为经典聚类算法假设数据是在单一的位置上,所以这些算法并不适合数据不能合并到单一位置的分布式数据环境。
本文提出的新聚类融合算法除了提高稳定性和聚类结果精确度外,它还可以用于分布式数据的聚类。分布式数据挖掘是数据挖掘的有趣的方面之一,尤其是当数据集因存储(通常数据挖掘涉及到大量的数据)或隐私性等原因,不能合并到一个位置。单一的经典聚类算法是不能处理这些情况的。我们的方法使用许多模式和聚类中心表示聚类,这使得我们的算法独特于现有的使用类标签标识每个模式或数据点的聚类融合方法。用聚类中心和大量模式表示的聚类,直接解决了标签对应问题,而不用像现有的大多数算法引用额外的技术。这种方法也节省了时间与空间,共识函数只需要聚类中心和数据点数量这些信息,它远远小于数据集中的实际数据点数量,这使得我们的算法适用于处理并行或分布式环境中的大量数据。实验结果表明,本文提出的聚类融合算法与k-means经典聚类算反比较,算法的精度性和稳定性更好。
论文章节安排如下：第一章介绍了数据挖掘和知识发现涉及的技术理念以及其应用。第二章着重于聚类和聚类融合,并对现有融合算法及技术进行了综述,第三章是提出的新聚类融合算法。第四章是实验与评估,第五章是结论。最后是感谢,参考文献和附录。
Organizing Data into sensible grouping is one of the most fundamental and crucial modes of understanding learning where by similar patterns are grouped together in one group and those not similar into another groups. This thesis presents a Statistical Consensus method for Cluster ensemble to improve clustering accuracy and stability and in some situations it can serve in distributed data mining in either case whether privacy issues or massive volume of data that cannot be pooled into a single location for processing or both. Ensemble methods have been popularly in supervised learning whereby it proved to reduce predictive errors to a considerable high margin compared to a single best classical predictor/learning model. Likewise recently intensive researches are in progress working on unsupervised learning (cluster ensembles) in which promising results are attained. Our proposed Clustering Ensemble technique comprises of four sub-parts on the way through the process in attaining final consensus clustering result.
The first part of our approach is generating the partitions in which K-means Clustering algorithm is employed with different initiations and run the algorithm several times. K-means algorithm is sensitive to initial parameters whereby different initialization leads to diverse clustering results from same dataset. The second part is the selection of a single best clustering among generated partitions. This is achieved by employing the objective function from the k-means clustering algorithm which is regarded as an error in this case. The k-means clustering states that, the minimum this error implies compact and well separated clusters which is the essence to clustering. Due to the lack of labels, the error is the only well proved mathematical measure in clustering quality analysis. The third part of the consensus method is the selection of consistent clusterings in which the inconsistency partitions or clustering are filtered out from the ensemble, only the consistency partitions/clustering are included in the consensus process. Here the information theory; Mutual Information (MI) is employed as criteria in selecting consistent partitions and the fourth part of our method is the consensus function. The final clustering result is achieved by fusing/combining the consistency partitions in the ensemble with a Statistical Consensus function.
Our research is based on Cluster Ensembles in which the concentration and focus is at improving the Accuracy of clustering result including Stability. Due to being the most influential development technique in Data Mining and Machine Learning, ensemble techniques combine multiple models into one which is usually more quality than the best of its components. Most Data Mining and Knowledge Discovery techniques emphasize more effort on model building rather than accuracy for stance in marketing, intrusion detections in networks and alike. But on contrary complex business intelligent systems like auditing, fraud detections including criminal detections do require much attention on clustering accuracy rather than the model. Any business intelligent system needs a quality clustering as its core technique and in most cases it involves huge volumes of data which sometimes can be in distributed environment. The issue is that, available classical clustering algorithms are not stable. Their instability leads to inaccurate clustering results at the same time they are not suitable for applications in distributed data environments whereby data cannot be pooled into a single location for processing because classical clustering techniques assumes that the data is located at single location.
The proposed new Consensus approach for Cluster Ensembles apart from producing stable and accurate clustering results, it offers capability for clustering distributed data sets. Distributed data mining is one of the interesting aspects in data mining especially when dataset cannot be pooled into a single location due to either storage issues (always data mining involve massive data) or privacy reasons. As prescribed before from previous paragraph, in these kinds of situations single classic clustering technique cannot handle. Our Consensus method uses number of patterns and cluster centers as representation for the clusters; this makes our technique to be unique from existing cluster ensemble methods in which they tend to use labels from each pattern or data point. The representation of clusters with cluster centers and its number of patterns resolves the label correspondence problem directly without introducing additional technique which is always the case in most existing cluster ensemble methods. This technique also serves time and storage since the only information needed for the consensus is the cluster's centers with their number of data points which is always much less that the actual number of data points in the dataset, this makes our consensus suitable for clustering massive volume of data in parallel and or distributed environment. Experiment results from real datasets shows that, our New Consensus clustering produces improved result in terms of accuracy and stability compared to its components which is classical k-means clustering algorithm in this case and can deal with massive volume of data in distributed environment.
The Thesis is presented as follows:The first chapter introduces the idea of Data Mining and Knowledge Discovery, techniques involved, applications including the challenges involves with a literature review. The second chapter focuses on the Ensembles and Clustering Ensembles reviewing the technique and existing ensemble methods citing their strengths and weaknesses, while the third chapter is presents the proposed Ensemble technique. The fourth chapter is about the Experiments undertaken and the Evaluation of results, with the fifth chapter giving the conclusion. The last three sections are about the Autography, the Appendices and the Acknowledgements respectively.
引文
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