Time-series clustering – A decade review

Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.     

An Effective Software Pipelining Algorithm for Clustered Embedded VLIW Processors

This paper proposes a software pipelining framework, CALiBeR (ClusterAware Load Balancing Retiming Algorithm), suitable for compilers targetingclustered embedded VLIW processors. CALiBeR can be used by embedded systemdesigners to explore different code optimization alternatives, that is, high-qualitycustomized retiming solutions for desired throughput and program memory sizerequirements, while minimizing register pressure. An extensive set of experimentalresults is presented, demonstrating that our algorithm compares favorablywith one of the best state-of-the-art algorithms, achieving up to 50% improvementin performance and up to 47% improvement in register requirements. In orderto empirically assess the effectiveness of clustering for high ILP applications,additional experiments are presented contrasting the performance achievedby software pipelined kernels executing on clustered and on centralized machines.     

A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections

Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters that accurately match the main tissue types that are independently determined by the clinician.     

Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

A clustering method for automatic biometric template selection

The problem addressed in this paper is the template selection and update in biometrics based on clustering. Template selection is a reliable method to reduce the number of templates used in a biometric system to account for variations observed in a person's biometric data. An efficient method based on clustering with automatic selection of the number of clusters is proposed in this work for finding subgroups of similar templates which are used for prototype selection.Experimental results confirm the advantage of the new method and the importance of adopting a procedure to perform template selection.     

Adherence clustering: an efficient method for mining market-basket clusters

We explore in this paper the efficient clustering of market-basket data. Different from those of the traditional data, the features of market-basket data are known to be of high dimensionality and sparsity. Without explicitly considering the presence of the taxonomy, most prior efforts on clustering market-basket data can be viewed as dealing with items in the leaf level of the taxonomy tree. Clustering transactions across different levels of the taxonomy is of great importance for marketing strategies as well as for the result representation of the clustering techniques for market-basket data. In view of the features of market-basket data, we devise in this paper a novel measurement, called the category-based adherence, and utilize this measurement to perform the clustering. With this category-based adherence measurement, we develop an efficient clustering algorithm, called algorithm k-todes, for market-basket data with the objective to minimize the category-based adherence. The distance of an item to a given cluster is defined as the number of links between this item and its nearest tode. The category-based adherence of a transaction to a cluster is then defined as the average distance of the items in this transaction to that cluster. A validation model based on information gain is also devised to assess the quality of clustering for market-basket data. As validated by both real and synthetic datasets, it is shown by our experimental results, with the taxonomy information, algorithm k-todes devised in this paper significantly outperforms the prior works in both the execution efficiency and the clustering quality as measured by information gain, indicating the usefulness of category-based adherence in market-basket data clustering.     

基于减聚类优化算法的无线传感网络分簇路由协议研究

无线传感网络是当前无线网络研究的热点领域,分簇路由协议作为降低网络能耗、提高网络生存时间的核心技术而备受关注。在深入分析已有算法存在诸多不足的基础上,本文提出了一种基于减聚类优化的分簇路由算法。本文首先建立一阶能耗模型,确定网络最优簇首数量;然后建立减聚类优化数学模型,确定可供选择的分簇方案;最后以最小化最大簇内距离方差作为评价指标,确定最优的分簇结果。仿真结果表明:相比经典的LEACH分簇路由协议,该算法划分的网络能够有效地降低网络能耗、提高网络生存时间,弥补已有算法的不足之处。     

基于SOM的模糊聚类在Web日志挖掘中的应用

将自组织映射神经网络（SOM）与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。     

基于聚类分析的新书推荐

针对大多数图书馆管理系统中的个性化服务中的新书推荐只是定时将新到馆的图书罗列出来让读者浏览进行推荐,推荐效果差的问题。本文在比较三大常用的推荐技术的优缺点后,采用基于聚类分析的推荐算法推荐新书,并具体给出实现的方法。     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.