Employees in companies can be divided into di erent communities, and those who frequently socialize with each other will be treated as close friends and are grouped in the same community. In the enterprise context, a large amount of information about the employees is available in both (1) o ine company internal sources and (2) online enterprise social networks (ESNs). Each of the information sources also contain multiple categories of employees' socialization activities at the same time. In this paper, we propose to detect the social communities of the employees in companies based on the broad learning se ing with both these online and o ine information sources simultaneously, and the problem is formally called the "Broad Learning based Enterprise Community Detection" (BL-Ecd) problem. To address the problem, a novel broad learning based community detection framework named "HeterogeneoUs Multi-sOurce ClusteRing" (Humor) is introduced in this paper. Based on the various enterprise social intimacy measures introduced in this paper, Humor detects a set of micro community structures of the employees based on each of the socialization activities respectively. To obtain the (globally) consistent community structure of employees in the company, Humor further fuses these micro community structures via two broad learning phases: (1) intra-fusion of micro community structures to obtain the online and o ine (locally) consistent communities respectively, and (2) inter-fusion of the online and o ine communities to achieve the (globally) consistent community structure of employees. Extensive experiments conducted on real-world enterprise datasets demonstrate our method can perform very well in addressing the BL-Ecd problem.
Clustering aims at grouping similar objects in the same cluster and many different clustering methods have also been proposed. One type is the hierarchical clustering methods @cite_12 , which include agglomerative hierarchical clustering methods @cite_26 and divisive hierarchical clustering methods @cite_26 . Meanwhile, according to the manner that the similarity measure is calculated, the hierarchical clustering methods can be further divided into single-link clustering @cite_41 , complete-link clustering @cite_38 and average-link clustering @cite_9 . Another type of clustering methods is partition-based methods, which include K-Means for instances with numerical attributes @cite_11 , K-Medoids for instances with categorical attributes @cite_5 , probabilistic clustering @cite_36 , as well as density-based clustering methods @cite_8 . Other clustering methods include grid-based clustering methods @cite_1 , constraint-based clustering @cite_16 and fuzzy clustering @cite_39 .
@cite_38 @cite_26 @cite_8 @cite_41 @cite_36 @cite_9 @cite_1 @cite_39 @cite_5 @cite_16 @cite_12 @cite_11
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We present a new method for clustering based on compression. The method does not use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the noncomputable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics, we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis. Classifying a set of objects into clusters can be done in numerous ways, producing different results. They can be visually compared using contingency tables [27], mosaicplots [13], fluctuation diagrams [15], tableplots [20] , (modified) parallel coordinates plots [28], Parallel Sets plots [18] or circos diagrams [19]. Unfortunately the interpretability of all these graphical displays decreases rapidly with the numbers of categories and clusterings. In his famous book A Semiology of Graphics [5] Bertin writes “the discovery of an ordered concept appears as the ultimate point in logical simplification since it permits reducing to a single instant the assimilation of series which previously required many instants of study”. Or in more everyday language, if you use good orderings you can see results immediately that with other orderings might take a lot of effort. This is also related to the idea of effect ordering [12], that data should be organised to reflect the effect you want to observe. This paper presents an efficient algorithm based on Bertin's idea and concepts related to Kendall's t [17], which finds informative joint orders for two or more nominal classification variables. We also show how these orderings improve the various displays and how groups of corresponding categories can be detected using a top-down partitioning algorithm. Different clusterings based on data on the environmental performance of cars sold in Germany are used for illustration. All presented methods are available in the R package extracat which is used to compute the optimized orderings for the example dataset. Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification (McLachlan & Basford, 1988) or competitive learning. In this paper, we give a comprehensive overview of competitive learning based clustering methods. Importance is attached to a number of competitive learning based clustering neural networks such as the self-organizing map (SOM), the learning vector quantization (LVQ), the neural gas, and the ART model, and clustering algorithms such as the C-means, mountain subtractive clustering, and fuzzy C-means (FCM) algorithms. Associated topics such as the under-utilization problem, fuzzy clustering, robust clustering, clustering based on non-Euclidean distance measures, supervised clustering, hierarchical clustering as well as cluster validity are also described. Two examples are given to demonstrate the use of the clustering methods. This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation. Techniques for partitioning objects into optimally homogeneous groups on the basis of empirical measures of similarity among those objects have received increasing attention in several different fields. This paper develops a useful correspondence between any hierarchical system of such clusters, and a particular type of distance measure. The correspondence gives rise to two methods of clustering that are computationally rapid and invariant under monotonic transformations of the data. In an explicitly defined sense, one method forms clusters that are optimally “connected,” while the other forms clusters that are optimally “compact.” The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Graph clustering techniques are very useful for detecting densely connected groups in a large graph. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties which are often heterogenous. In this paper, we propose a novel graph clustering algorithm, SA-Cluster, based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparison with the state-of-the-art graph clustering and summarization methods. Clustering is a widely used technique in data mining applications to discover patterns in the underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either continuous or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining problems. In this paper, we propose a distance measure that enables clustering data with both continuous and categorical attributes. This distance measure is derived from a probabilistic model that the distance between two clusters is equivalent to the decrease in log-likelihood function as a result of merging. Calculation of this measure is memory efficient as it depends only on the merging cluster pair and not on all the other clusters. [8] proposed a clustering method named BIRCH that is especially suitable for very large datasets. We develop a clustering algorithm using our distance measure based on the framework of BIRCH. Similar to BIRCH, our algorithm first performs a pre-clustering step by scanning the entire dataset and storing the dense regions of data records in terms of summary statistics. A hierarchical clustering algorithm is then applied to cluster the dense regions. Apart from the ability of handling mixed type of attributes, our algorithm differs from BIRCH in that we add a procedure that enables the algorithm to automatically determine the appropriate number of clusters and a new strategy of assigning cluster membership to noisy data. For data with mixed type of attributes, our experimental results confirm that the algorithm not only generates better quality clusters than the traditional k-means algorithms, but also exhibits good scalability properties and is able to identify the underlying number of clusters in the data correctly. The algorithm is implemented in the commercial data mining tool Clementine 6.0 which supports the PMML standard of data mining model deployment. Most clustering algorithms produce a single clustering for a given dataset even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult problem of uncovering disparate clusterings from the data in a totally unsupervised manner. We propose two new approaches for this problem. In the first approach, we aim to find good clusterings of the data that are also decorrelated with one another. To this end, we give a new and tractable characterization of decorrelation between clusterings, and present an objective function to capture it. We provide an iterative “decorrelated” k-means type algorithm to minimize this objective function. In the second approach, we model the data as a sum of mixtures and associate each mixture with a clustering. This approach leads us to the problem of learning a convolution of mixture distributions. Though the latter problem can be formulated as one of factorial learning 8, 13, 16, the existing formulations and methods do not perform well on many real high-dimensional datasets. We propose a new regularized factorial-learning framework that is more suitable for capturing the notion of disparate clusterings in modern, high-dimensional datasets. Furthermore, we provide kernelized version of both of our algorithms. The resulting algorithms do well in uncovering multiple clusterings, and are much improved over existing methods. We evaluate our methods on two real-world datasets—a music dataset from the text-mining domain, and a portrait dataset from the computer-vision domain. Our methods achieve a substantially higher accuracy than existing factorial learning as well as traditional clustering algorithms. Copyright © 2008 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000-000, 2008 Abstract Clustering based on grid and density for multi-density datasets plays a key role in data mining. In this work, a clustering method that consists of a grid ranking strategy based on local density and priority-based anchor expansion is proposed. In the proposed method, grid cells are ranked first according to local grid properties so the dataset is transformed into a ranked grid. An adjusted shifting grid is then introduced to calculate grid cell density. A cell expansion strategy that simulates the growth of bacterial colony is used to improve the completeness of each cluster. An adaptive technique is finally adopted to handle noisy cells to ensure accurate clustering. The accuracy, parameter sensitivity and computation cost of the proposed algorithm are analysed. The performance of the proposed algorithm is then compared to other clustering methods using four two-dimensional datasets, and the applicability of the proposed method to high-dimensional, large-scale dataset is discussed. Experimental results demonstrate that the proposed algorithm shows good performance in terms of accuracy, de-noising capability, robustness (parameters sensitivity) and computational efficiency. In addition, the results show that the proposed algorithm can handle effectively the problem of multi-density clustering. Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may not be the optimal one for data clustering and lead to the suboptimal results. In this paper, we propose a novel clustering model to learn the data similarity matrix and clustering structure simultaneously. Our new model learns the data similarity matrix by assigning the adaptive and optimal neighbors for each data point based on the local distances. Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the data similarity matrix, such that the connected components in the resulted similarity matrix are exactly equal to the cluster number. We derive an efficient algorithm to optimize the proposed challenging problem, and show the theoretical analysis on the connections between our method and the K-means clustering, and spectral clustering. We also further extend the new clustering model for the projected clustering to handle the high-dimensional data. Extensive empirical results on both synthetic data and real-world benchmark data sets show that our new clustering methods consistently outperforms the related clustering approaches. As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on information network data, and each has been a hot topic by itself. However, ranking objects globally without considering which clusters they belong to often leads to dumb results, e.g., ranking database and computer architecture conferences together may not make much sense. Similarly, clustering a huge number of objects (e.g., thousands of authors) in one huge cluster without distinction is dull as well. In this paper, we address the problem of generating clusters for a specified type of objects, as well as ranking information for all types of objects based on these clusters in a multi-typed (i.e., heterogeneous) information network. A novel clustering framework called RankClus is proposed that directly generates clusters integrated with ranking. Based on initial K clusters, ranking is applied separately, which serves as a good measure for each cluster. Then, we use a mixture model to decompose each object into a K-dimensional vector, where each dimension is a component coefficient with respect to a cluster, which is measured by rank distribution. Objects then are reassigned to the nearest cluster under the new measure space to improve clustering. As a result, quality of clustering and ranking are mutually enhanced, which means that the clusters are getting more accurate and the ranking is getting more meaningful. Such a progressive refinement process iterates until little change can be made. Our experiment results show that RankClus can generate more accurate clusters and in a more efficient way than the state-of-the-art link-based clustering methods. Moreover, the clustering results with ranks can provide more informative views of data compared with traditional clustering. This paper presents a detailed empirical study of 12 generative approaches to text clustering, obtained by applying four types of document-to-cluster assignment strategies (hard, stochastic, soft and deterministic annealing (DA) based assignments) to each of three base models, namely mixtures of multivariate Bernoulli, multinomial, and von Mises-Fisher (vMF) distributions. A large variety of text collections, both with and without feature selection, are used for the study, which yields several insights, including (a) showing situations wherein the vMF-centric approaches, which are based on directional statistics, fare better than multinomial model-based methods, and (b) quantifying the trade-off between increased performance of the soft and DA assignments and their increased computational demands. We also compare all the model-based algorithms with two state-of-the-art discriminative approaches to document clustering based, respectively, on graph partitioning (CLUTO) and a spectral coclustering method. Overall, DA and CLUTO perform the best but are also the most computationally expensive. The vMF models provide good performance at low cost while the spectral coclustering algorithm fares worse than vMF-based methods for a majority of the datasets.