A Hierarchical Clustering Algorithm Based on Saturated Neighbor Graph

Based on the traditional grid density clustering algorithm, proposing A Data Stream Clustering Algorithm Based on Density and Extended Grid(DEGDS). The algorithm combines the advantages of grid clustering algorithm and density clustering algorithm, by improving the defects of clustering parameters by artificially set, get any shape of the cluster. The algorithm uses the local density of each sample point and the distance from the other sample points, determining the number of clustering centers in the grid, and realizing the automatic determination of the clustering center, which avoids the influence of improper selection of initial centroid on clustering results. And in the process of combining the Spark parallel framework for partitioning the data to achieve its parallelization. For data points clustered outside the grid, the clustering within the grid has been effectively expanded by extending the grid, to ensure the accuracy of clustering. Introduced density estimation is connected and grid boundaries to merging grid, saving memory consumption. Using the attenuation factor to incremental update grid density, reflect the evolution of spatial data stream. The experimental results show that compared with the traditional clustering algorithm, the DEGDS algorithm has a large performance improvement in accuracy and efficiency, and can be effectively for large data clustering.

Isomap is a classic and representative manifold learning algorithm for nonlinear dimensionality reduction, which aims to circumvent the problem of “the curse of dimensionality” and attempts to recover the intrinsic structure hidden in high-dimensional data based on the assumption that data lie in or near a single manifold. However, Isomap fails to work when data set consists of multi-clusters or multiple sub-manifolds due to the requirement of preserving global geometric structure within data points. Several extended Isomap algorithms have been proposed to handle this issue, such as D-C Isoamp, M-Isomap, etc.. Here, we describe a new multi-manifolds learning algorithm by using a universal concept of neighbor: natural nearest neighbor (3N neighbor), which can be utilized automatically to construct a single or multiple adaptive neighborhood graphs that roughly coincide with the corresponding real multi-manifolds. The new algorithm preserves local adaptive neighborhood graph structure in terms of multi-manifolds as good as possible, in contrast to the previous algorithms, no category information about multi-manifolds are required. So it is a natural extension to the original Isomap, and also an adaptive multi-manifolds learning algorithm without the use of free parameter.

Each clustering algorithm usually optimizes a qualification metric during its learning process. The qualification metric in traditional clustering algorithms considers all the features of under-consideration dataset equally; it means each feature participates in the clustering process equivalently. Considering that some features have more information than the others in a dataset (due to their lower information or their higher variances, etc.), we proposes a fuzzy weighted clustering algorithm. We name this new clustering algorithm, Fuzzy Weighted Locally Adaptive Clustering (FWLAC) algorithm. The proposed FWLAC algorithm is capable of handling imbalanced clustering. However, FWLAC algorithm suffers from its sensitivity to the two parameters that should be tuned manually. The performance of FWLAC algorithm is affected by well-tuning of its parameters. So the paper proposes two solutions to well-tuning of its two parameters. In the first solution, we propose a simple clustering ensemble framework to show the sensitivity of the WLAC algorithm to its manual well-tuning. Although it is not a try-and-error procedure, it is like a grid search, where we use different pairs of values for both parameters h 1 and h 2. Per each pair of values for parameters h 1 and h 2, the algorithm produces a partitioning. So after the grid search, we obtain a large number of partitionings. We break any of the partitionings into its clusters, and then they form an ensemble of clusters. Finally the consensus partitioning is extracted from them by a consensus function. The algorithm is not data dependent at all. For all datasets, we use a similar grid search and a similar set of values for the parameters h 1 and h 2. In this way we have proposed an alternative solution to parameter selection. We use a selection phase to select/remove some clusters from our ensemble of clusters to obtain an elite ensemble of clusters. To do this, a stability measure, normalized mutual information (NMI), was used to validate a cluster. The paper shows the effectiveness of the proposed clustering frameworks both theoretically and experimentally.

Density Peaks Clustering (DPC) algorithm identifies cluster centers based on local density and relative distance, disregarding the effect of the sample's environment on its density. This makes it challenging to identify cluster centers in low-density regions. Moreover, the single-step allocation strategy of DPC algorithm lacks fault tolerance. If a sample point is incorrectly allocated, it can trigger a cascade of subsequent allocation error. To solve the aforementioned issue, a density peak clustering algorithm based on natural neighbors and diffusion allocation (NNDA-DPC) is proposed. Firstly, the concept of natural neighbors is introduced, and the local density of sample points is redefined by considering the density of sample points and the environment in which the sample points are located. This is done to reduce the influence of the sparsity of class clusters on the selection of class cluster centers. The clustering centers are automatically determined by computing the decision value. Secondly, the natural neighbor points of the selected clustering centers are assigned to the corresponding clustering centers to form the initial microclusters. Finally, the initial microclusters are formed based on the remaining sample points and the initial clusters. The final clustering result is obtained by assigning the sample points based on the correlation between the remaining sample points and the initial clusters, which helps prevent the cascading effect of assignment errors. We compared the NNDA-DPC algorithm with DPC and its enhanced versions on artificial and UCI datasets. The experimental results demonstrate that the NNDA-DPC algorithm can efficiently cluster the datasets.

A classification algorithm for environmental sound recordings or “soundscapes” is outlined. An ant clustering approach is proposed, in which the behavior of the ants is governed by fuzzy rules. These rules are optimized by a genetic algorithm specially designed in order to achieve the optimal set of homogeneous clusters. Soundscape similarity is expressed as fuzzy resemblance of the shape of the sound pressure level histogram, the frequency spectrum and the spectrum of temporal fluctuations. These represent the loudness, the spectral and the temporal content of the soundscapes. Compared to traditional clustering methods, the advantages of this approach are that no a priori information is needed, such as the desired number of clusters, and that a flexible set of soundscape measures can be used. The clustering algorithm was applied to a set of 1116 acoustic measurements in 16 urban parks of Stockholm. The resulting clusters were validated against visitor’s perceptual measurements of soundscape quality.

Recommendation system for tourist spots has very high potential value including social and economic benefits. The traditional clustering algorithms were usually used to build a recommendation system. However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily. Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference. To address the problem, we focused our research work on designing a novel recommendation system for tourist spots. First a new dataset named “Smart Travel” is created for the following experiments. Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes. A new recommendation list named LA is generated in turn by fitting the excavated the user preference. More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings. And a new recommendation list named LB is generated in turn on the basis of rating predictions. Finally, the two lists LA and LB are fused together to boost the final recommendation performance. Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor. The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.

The innovative concept of cloud computing in service model and technology has brought profound changes to the work of the library. In order to further realize the goal of library information resource sharing, this paper proposes a clustering algorithm that effectively evaluates the resource sharing operation mechanism, and effectively combines resource sharing cloud service with cloud computing. Based on the analysis of the key technologies of library information resource sharing cloud service, the cloud computing model algorithm is proposed and constructed to design the cloud service sharing model, and the IaaS cloud computing platform and the cost utility of the underlying support platform of SaaS and PaaS are developed. The library information resource sharing cloud service system is designed, and the performance of the system is analyzed and verified through experiments. The experimental results show that the running time of the algorithm on the four data sets is more than 30 times faster than the traditional clustering algorithm, which indicates that the algorithm is very effective compared with the traditional clustering algorithm. The research provides effective technical support and theoretical reference for the realization of library information resource sharing cloud service.

Simulated annealing spectral clustering algorithm for image segmentation

Clustering algorithms divide data into meaningful or useful groups, called clusters, such that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized.These discovered clusters can be used to explain the characteristics of the underlying data distribution and thus serve as the foundation for various data mining and analysis techniques.The applications of clustering include characterization of different customer groups based upon purchasing patterns, categorization of documents on the World Wide Web, grouping of genes and proteins that have similar functionality, grouping of spatial locations prone to earth quakes from seismological data, etc. What is CLUTOCLUTO is a software package for clustering low and high dimensional datasets and for analyzing the characteristics of the various clusters.CLUTO provides three different classes of clustering algorithms that operate either directly in the object's feature space or in the object's similarity space.These algorithms are based on the partitional, agglomerative, and graphpartitioning paradigms.A key feature in most of CLUTO's clustering algorithms is that they treat the clustering problem as an optimization process which seeks to maximize or minimize a particular clustering criterion function defined either globally or locally over the entire clustering solution space.CLUTO provides a total of seven different criterion functions that can be used to drive both partitional and agglomerative clustering algorithms, that are described and analyzed in [6,5].Most of these criterion functions have been shown to produce high quality clustering solutions in high dimensional datasets, especially those arising in document clustering.In addition to these criterion functions, CLUTO provides some of the more traditional local criteria (e.g., single-link, complete-link, and UPGMA) that can be used in the context of agglomerative clustering.Furthermore, CLUTO provides graph-partitioning-based clustering algorithms that are well-suited for finding clusters that form contiguous regions that span different dimensions of the underlying feature space.An important aspect of partitional-based criterion-driven clustering algorithms is the method used to optimize this criterion function.CLUTO uses a randomized incremental optimization algorithm that is greedy in nature, has low computational requirements, and has been shown to produce high-quality clustering solutions [6].CLUTO's graphpartitioning-based clustering algorithms utilize high-quality and efficient multilevel graph partitioning algorithms derived from the METIS and hMETIS graph and hypergraph partitioning algorithms [4,3].CLUTO also provides tools for analyzing the discovered clusters to understand the relations between the objects assigned to each cluster and the relations between the different clusters, and tools for visualizing the discovered clustering solutions.CLUTO can identify the features that best describe and/or discriminate each cluster.These set of features can be used to gain a better understanding of the set of objects assigned to each cluster and to provide concise summaries about the cluster's contents.Moreover, CLUTO provides visualization capabilities that can be used to see the relationships between the clusters, objects, and features.CLUTO's algorithms have been optimized for operating on very large datasets both in terms of the number of objects as well as the number of dimensions.This is especially true for CLUTO's algorithms for partitional clustering.These algorithms can quickly cluster datasets with several tens of thousands objects and several thousands of dimensions.Moreover, since most high-dimensional datasets are very sparse, CLUTO directly takes into account this sparsity and requires memory that is roughly linear on the input size.CLUTO's distribution consists of both stand-alone programs (vcluster and scluster) for clustering and analyzing these clusters, as well as, a library via which an application program can access directly the various clustering and analysis algorithms implemented in CLUTO. Outline of CLUTO's ManualCLUTO's manual is organized as follows.Section 2 describe the major changes from the previous release.Section 3 describes the stand-alone programs provided by CLUTO, and discusses its various options and analysis capabilities.Section 4 describes the type of clusters that CLUTO's algorithms can find, and discusses their scalability.Section 5 de-

<span>Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.</span>

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in order to base the algorithm S-NN (Similar Nearest Neighbours). Traditional clustering algorithms are very sensitive to the user-defined parameters and an expert knowledge is required to choose the values. Frequently, these algorithms are fragile in the presence of outliers and any adjust well to spherical shapes. Experiments have shown that S-NN is accurate discovering arbitrary shapes and density clusters, since it takes into account the internal features of each cluster, and it does not depend on a user-supplied static model. S-NN achieve this by collecting the nearest neighbours with the same label until the enemy is found (it has not the same label). The determinism and the results offered to the researcher turn it into a valuable tool for the representation of the inherent knowledge to the labelled databases.

Aiming at the problems that the initial cluster centers are randomly selected and the number of clusters is manually determined in traditional clustering algorithm, which results in unstable clustering results, we propose an improved clustering algorithm based on density peak and nearest neighbors. Firstly, an improved density peak clustering method is proposed to optimize the cutoff distance and local density of data points. It avoids that random selection of initial cluster centers is easy to fall into the local optimal solution. Furthermore, a K-value selection method is presented to choose the optimal number of clusters, which is determined by the sum of the squared errors within the clusters. Finally, we employ the idea of the K-nearest neighbors to carry out the assignment for outliers. Experiments on the UCI real data sets indicate that our proposed algorithm can achieve better clustering results compared with several known algorithms.

An improved density peaks clustering algorithm based on natural neighbor with a merging strategy

Social network analysis is a new research field in data mining. The clustering in social network analysis is different from traditional clustering. It requires grouping objects into classes based on their links as well as their attributes. While traditional clustering algorithms group objects only based on objects’ similarity, and it can't be applied to social network analysis. So on the basis of BSP (business system planning) clustering algorithm, a social network clustering analysis algorithm is proposed. The proposed algorithm, different from traditional clustering algorithms, can group objects in a social network into different classes based on their links and identify relation among classes.

Recently, many data mining techniques were revisited to cope with the new big data challenges. Nearly all of these algorithms considered the efficiency of the mining algorithm to survive the increasing size of the data. However, as the dimensionality of the data increases, not only the efficiency but also the effectiveness of traditional mining algorithms is compromised. For instance, clusters hidden in some subspaces are hard to be detected using traditional clustering algorithms, as the dimensionality of the data increases. In this paper, we consider both the huge size, and the high dimensionality of big data by providing a novel solution that presents a three-phase model for subspace stream clustering algorithms. Our novel model, overcomes the huge size of the big data in its first phase, by continuously applying a streaming concept over the huge data objects, and summarizing them into micro-clusters. Then, after each certain batch of data, or after upon a user request, the second phase is applied over the data summarized in micro-clusters, to reconstruct the current distribution of the data out of the current summaries. In the third phase, a subspace clustering algorithm is applied to overcome the high dimensionality of the data, and to find hidden clusters within some subspace. An extensive evaluation study over different scenarios that follow our model over a big data set is performed.KeywordsSubspace stream clusteringStreaming big dataReal-time streaming analysis of big data