基于最小生成树的密度聚类算法研究

传统DBSCAN算法对密度分布不均匀的不平衡数据集的聚类效果并不理想,同时传统算法的聚类结果对邻域半径(Eps)以及核心点阈值(MinPts)敏感.针对以上问题,改进了传统算法,提出了一种基于最小生成树的密度聚类算法(MST-DBSCAN).由于对象之间的距离对聚类结果影响较大,为了更好地表示对象之间的距离特性,首先使...     

Interval set clustering

Rough k-means clustering describes uncertainty by assigning some objects to more than one cluster. Rough cluster quality index based on decision theory is applicable to the evaluation of rough clustering. In this paper we analyze rough k-means clustering with respect to the selection of the threshold, the value of risk for assigning an object and uncertainty of objects. According to the analysis, clusters presented as interval sets with lower and upper approximations in rough k-means clustering are not adequate to describe clusters. This paper proposes an interval set clustering based on decision theory. Lower and upper approximations in the proposed algorithm are hierarchical and constructed as outer-level approximations and inner-level ones. Uncertainty of objects in out-level upper approximation is described by the assignment of objects among different clusters. Accordingly, ambiguity of objects in inner-level upper approximation is represented by local uniform factors of objects. In addition, interval set clustering can be improved to obtain a satisfactory clustering result with the optimal number of clusters, as well as optimal values of parameters, by taking advantage of the usefulness of rough cluster quality index in the evaluation of clustering. The experimental results on synthetic and standard data demonstrate how to construct clusters with satisfactory lower and upper approximations in the proposed algorithm. The experiments with a promotional campaign for the retail data illustrates the usefulness of interval set clustering for improving rough k-means clustering results.     

一种不精确数据的聚类挖掘方法*

在聚类过程中考虑到数据的非确定性,提出了一种改进的K-平均算法——FK-算法。FK-算法思想是减小总均方误差的期望值E(SSE),需特别说明的是对数据对象xi 采用在非确定区域内用非确定密度概率函数pdf f(xi)进行描述。用FK-算法对非确定运动模式的运动对象进行了分析,实验表明考虑数据的非确定因素,在聚类分析处理时有比较精确的结果。     

A new initialization method for categorical data clustering

In clustering algorithms, choosing a subset of representative examples is very important in data set. Such “exemplars” can be found by randomly choosing an initial subset of data objects and then iteratively refining it, but this works well only if that initial choice is close to a good solution. In this paper, based on the frequency of attribute values, the average density of an object is defined. Furthermore, a novel initialization method for categorical data is proposed, in which the distance between objects and the density of the object is considered. We also apply the proposed initialization method to k-modes algorithm and fuzzy k-modes algorithm. Experimental results illustrate that the proposed initialization method is superior to random initialization method and can be applied to large data sets for its linear time complexity with respect to the number of data objects.     

Improving DBSCAN’s execution time by using a pruning technique on bit vectors

Clustering is the process of assigning a set of physical or abstract objects into previously unknown groups. The goal of clustering is to group similar objects into the same clusters and dissimilar objects into different clusters. Similarities between objects are evaluated by using the attribute values of objects. There are many clustering algorithms in the literature; among them, DBSCAN is a well known density-based clustering algorithm. We improve DBSCAN’s execution time performance for binary data sets and Hamming distances. We achieve considerable speed gains by using a novel pruning technique, as well as bit vectors, and binary operations. Our novel method effectively discards distant neighbors of an object and computes only the distances between an object and its possible neighbors. By discarding distant neighbors, we avoid unnecessary distance computations and use less CPU time when compared with the conventional DBSCAN algorithm. However, the accuracy of our method is identical to that of the original DBSCAN. Experimental test results on real and synthetic data sets demonstrate that, by using our pruning technique, we obtain considerably faster execution time results compared to DBSCAN.     

Proximity-based k-partitions clustering with ranking for document categorization and analysis

As one of the most fundamental yet important methods of data clustering, center-based partitioning approach clusters the dataset into k subsets, each of which is represented by a centroid or medoid. In this paper, we propose a new medoid-based k-partitions approach called Clustering Around Weighted Prototypes (CAWP), which works with a similarity matrix. In CAWP, each cluster is characterized by multiple objects with different representative weights. With this new cluster representation scheme, CAWP aims to simultaneously produce clusters of improved quality and a set of ranked representative objects for each cluster. An efficient algorithm is derived to alternatingly update the clusters and the representative weights of objects with respect to each cluster. An annealing-like optimization procedure is incorporated to alleviate the local optimum problem for better clustering results and at the same time to make the algorithm less sensitive to parameter setting. Experimental results on benchmark document datasets show that, CAWP achieves favorable effectiveness and efficiency in clustering, and also provides useful information for cluster-specified analysis.     

Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework

The well-known Fuzzy C-Means (FCM) algorithm for data clustering has been extended to Evidential C-Means (ECM) algorithm in order to work in the belief functions framework with credal partitions of the data. Depending on data clustering problems, some barycenters of clusters given by ECM can become very close to each other in some cases, and this can cause serious troubles in the performance of ECM for the data clustering. To circumvent this problem, we introduce the notion of imprecise cluster in this paper. The principle of our approach is to consider that objects lying in the middle of specific classes (clusters) barycenters must be committed with equal belief to each specific cluster instead of belonging to an imprecise meta-cluster as done classically in ECM algorithm. Outliers object far away of the centers of two (or more) specific clusters that are hard to be distinguished, will be committed to the imprecise cluster (a disjunctive meta-cluster) composed by these specific clusters. The new Belief C-Means (BCM) algorithm proposed in this paper follows this very simple principle. In BCM, the mass of belief of specific cluster for each object is computed according to distance between object and the center of the cluster it may belong to. The distances between object and centers of the specific clusters and the distances among these centers will be both taken into account in the determination of the mass of belief of the meta-cluster. We do not use the barycenter of the meta-cluster in BCM algorithm contrariwise to what is done with ECM. In this paper we also present several examples to illustrate the interest of BCM, and to show its main differences with respect to clustering techniques based on FCM and ECM.     

SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

A K-partitioning algorithm for clustering large-scale spatio-textual data

The volume of spatio-textual data is drastically increasing in these days, and this makes more and more essential to process such a large-scale spatio-textual dataset. Even though numerous works have been studied for answering various kinds of spatio-textual queries, the analyzing method for spatio-textual data has rarely been considered so far. Motivated by this, this paper proposes a k-means based clustering algorithm specialized for a massive spatio-textual data. One of the strong points of the k-means algorithm lies in its efficiency and scalability, implying that it is appropriate for a large-scale data. However, it is challenging to apply the normal k-means algorithm to spatio-textual data, since each spatio-textual object has non-numeric attributes, that is, textual dimension, as well as numeric attributes, that is, spatial dimension. We address this problem by using the expected distance between a random pair of objects rather than constructing actual centroid of each cluster. Based on our experimental results, we show that the clustering quality of our algorithm is comparable to those of other k-partitioning algorithms that can process spatio-textual data, and its efficiency is superior to those competitors.     

An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems

Provision of training data sets is one of the core requirements for event-based supervised NILM (Non-Intrusive Load Monitoring) algorithms. Due to diversity in appliances’ technologies, in-situ training by users is often required. This process might require continuous user-interaction to ensure that a high quality training data set is provided. Pre-populating a training data set could potentially reduce the need for user-system interaction. In this study, a heuristic unsupervised clustering algorithm is presented and evaluated to enable autonomous partitioning of appliances signature space (i.e. feature space) for applications in electricity consumption disaggregation. The algorithm is based on hierarchical clustering and uses the characteristics of a cluster binary tree to determine the distance threshold for pruning the tree without a priori information. The algorithm determines the partition of a feature space recursively to account for multi-scale nature of the binary cluster tree. Evaluation of the algorithm was carried out using metrics for accuracy and cluster quality (proposed in this study) on a fully labeled data set that was collected and processed in a real residential setting. The algorithm performance in accurate partitioning of the feature space and the effect of different feature extraction techniques were presented and discussed.     

Long distance bigram models applied to word clustering

Two novel word clustering techniques are proposed which employ long distance bigram language models. The first technique is built on a hierarchical clustering algorithm and minimizes the sum of Mahalanobis distances of all words after a cluster merger from the centroid of the class created by merging. The second technique resorts to probabilistic latent semantic analysis (PLSA). Next, interpolated long distance bigrams are considered in the context of the aforementioned clustering techniques. Experiments conducted on the English Gigaword corpus (second edition) demonstrate that: (1) the long distance bigrams, when employed in the two clustering techniques under study, yield word clusters of better quality than the baseline bigrams; (2) the interpolated long distance bigrams outperform the long distance bigrams in the same respect; (3) the long distance bigrams perform better than the bigrams, which incorporate trigger-pairs selected at various distances; and (4) the best word clustering is achieved by the PLSA that employs interpolated long distance bigrams. Both proposed techniques outperform spectral clustering based on k-means. To assess objectively the quality of the created clusters, relative cluster validity indices are estimated as well as the average cluster sense precision, the average cluster sense recall, and the F-measure are computed by exploiting ground truth extracted from the WordNet.     

Efficient top-k similarity join processing over multi-valued objects

The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and data mining. Given two sets of objects $\mathcal U$ and $\mathcal V$ , a top-k similarity join returns k pairs of most similar objects from $\mathcal U \times \mathcal V$ . In the conventional model of top-k similarity join processing, an object is usually regarded as a point in a multi-dimensional space and the similarity is measured by some simple distance metrics like Euclidean distance. However, in many applications an object may be described by multiple values (instances) and the conventional model is not applicable since it does not address the distributions of object instances. In this paper, we study top-k similarity join over multi-valued objects. We apply two types of quantile based distance measures, ?-quantile distance and ?-quantile group-base distance, to explore the relative instance distribution among the multiple instances of objects. Efficient and effective techniques to process top-k similarity joins over multi-valued objects are developed following a filtering-refinement framework. Novel distance, statistic and weight based pruning techniques are proposed. Comprehensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our techniques.     

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

This article presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor(DBRkNN) queries, which extend the bichromatic reverse nearest neighbor queries. Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P′ of P such that k nearest neighbors of each object in P′ include q and each object in P′ has a direction toward q within a pre-defined distance. We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query. Our method utilizes a grid-based index to cluster the spatial objects, and the B⁺-tree to index the direction angle. We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries. In the filtering step, DART eliminates all the objects that are away from the query object more than a pre-defined distance, or have an invalid direction angle. In the refinement step, remaining objects are verified whether the query object is actually one of the k nearest neighbors of them. As a major extension of DART, we also present an improved algorithm, called DART+, for DBRkNN queries. From extensive experiments with several datasets, we show that DART outperforms an R-tree-based naive algorithm in both indexing time and query processing time. In addition, our extension algorithm, DART+, also shows significantly better performance than DART.     

Categorical top-<Emphasis Type="Italic">k</Emphasis> spatial influence query

The influence of a spatial facility object depicts the importance of the object in the whole data space. In this paper, we present a novel definition of object influence in applications where objects are of different categories. We study the problem of Spatial Influence Query which considers the contribution of an object in forming functional units consisting of a given set of objects with different categories designated by users. We first show that the problem of spatial influence query is NP-hard with respect to the number of object categories in the functional unit. To tackle the computational hardness, we develop an efficient framework following two main steps, possible participants finding and optimal functional unit computation. Based on this framework, for the first step, novel and efficient pruning techniques are developed based on the nearest neighbor set (NNS) approach. To find the optimal functional unit efficiently, we propose two algorithms, an exact algorithm and an efficient approximate algorithm with performance guarantee. Comprehensive experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques.     

Effective fuzzy c-means clustering algorithms for data clustering problems

Clustering is a well known technique in identifying intrinsic structures and find out useful information from large amount of data. One of the most extensively used clustering techniques is the fuzzy c-means algorithm. However, computational task becomes a problem in standard objective function of fuzzy c-means due to large amount of data, measurement uncertainty in data objects. Further, the fuzzy c-means suffer to set the optimal parameters for the clustering method. Hence the goal of this paper is to produce an alternative generalization of FCM clustering techniques in order to deal with the more complicated data; called quadratic entropy based fuzzy c-means. This paper is dealing with the effective quadratic entropy fuzzy c-means using the combination of regularization function, quadratic terms, mean distance functions, and kernel distance functions. It gives a complete framework of quadratic entropy approaching for constructing effective quadratic entropy based fuzzy clustering algorithms. This paper establishes an effective way of estimating memberships and updating centers by minimizing the proposed objective functions. In order to reduce the number iterations of proposed techniques this article proposes a new algorithm to initialize the cluster centers.In order to obtain the cluster validity and choosing the number of clusters in using proposed techniques, we use silhouette method. First time, this paper segments the synthetic control chart time series directly using our proposed methods for examining the performance of methods and it shows that the proposed clustering techniques have advantages over the existing standard FCM and very recent ClusterM-k-NN in segmenting synthetic control chart time series.     

一种改进的粗k 均值聚类算法

Lingras提出的粗K均值聚类算法易受随机初始聚类中心和离群点的影响,可能出现一致性和无法收敛的聚类结果.对此,提出一种改进的粗K均值算法,选择潜能最大的K个对象作为初始的聚类中心,根据数据对象与聚类中心的相对距离来确定其上下近似归属,使边界区域的划分更合理.定义了广义分类正确率,该指标同时考虑了下近似集和边界区域中的对象,评价算法性能更准确.仿真实验结果表明,该算法分类正确率高,收敛速度快,能够克服离群点的不利影响.     

基于残差和密度网格的簇心自确认聚类算法

为了解决DPC（Clustering by fast search and ?nd of Density Peaks）算法中依赖截断距离、计算复杂度大和需要人工选取簇心的问题，提出了基于残差和密度网格的簇心自确认聚类算法。将数据对象映射到网格上，用网格对象作为聚类对象，删除不含任何信息的网格对象；用特定方式计算网格对象的密度值和距离值；接着通过残差分析确定含有簇心的网格对象；用与非边缘点的距离和自变动的阈值来处理网格边缘点和噪声点。仿真实验表明所提出的算法与一些其他聚类算法对比，有着较高的聚类精度和较低的时间复杂度。     

基于密度的聚类中心自动确定的混合属性数据聚类算法研究

面对广泛存在的混合属性数据,现有大部分混合属性聚类算法普遍存在聚类 质量低、聚类算法参数依赖性大、聚类类别个数和聚类中心无法准确自动确定等问题,针对 这些问题本文提出了一种基于密度的聚类中心自动确定的混合属性数据 聚类算法.该算法通过分析混合属性数据特征,将混合属性数据分为数 值占优、分类占优和均衡型混合属性数据三类,分析不同情况的特征选取 相应的距离度量方式.在计算数据集各个点的密度和距离分布图基础 上,深入分析获得规律: 高密度且与比它更高密度的数据点有较大距离的数 据点最可能成为聚类中心,通过线性回归模型和残差分析确定奇异 点,理论论证这些奇异点即为聚类中心,从而实现了自动确定聚类中心.采 用粒子群算法(Particle swarm optimization, PSO)寻找最优dc值,通过参数dc能够计算得到 任意数据对象的密度和到比它密度更高的点的最小距离,根据聚类 中心自动确定方法确定每个簇中心,并将其他点按到最近邻的更高 密度对象的最小距离划分到相应的簇中,从而实现聚类.最终将本文 提出算法与其他现有的多种混合属性聚类算法在多个数据集上进行 算法性能比较,验证本文提出算法具有较高的聚类质量.     

On using cache conscious clustering for improving OODBMS performance

The two main techniques of improving I/O performance of Object Oriented Database Management Systems (OODBMS) are clustering and buffer replacement. Clustering is the placement of objects accessed near to each other in time into the same page. Buffer replacement involves the selection of a page to be evicted, when the buffer is full. The page evicted ideally should be the page needed least in the future. These two techniques both influence the likelihood of a requested object being memory resident. We believe an effective way of reducing disk I/O is to take advantage of the synergy that exists between clustering and buffer replacement. Hence, we design a framework, whereby clustering algorithms incorporating buffer replacement cache behaviour can be conveniently employed for enhancing the I/O performance of OODBMS. We call this new type of clustering algorithm, Cache Conscious Clustering (C3). In this paper, we present the C3 framework, and a C3 algorithm that we have developed, namely C3-GP. We have tested C3-GP against three well known clustering algorithms. The results show that C3-GP out performs them by up to 40% when using popular buffer replacement algorithms such as LRU and CLOCK. C3-GP offers the same performance as the best existing clustering algorithm when the buffer size compared to the database size is very small.