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When using arbitrarily oriented subspace clustering algorithms one obtains a partitioning of a given data set and for each partition its individual subspace. Since clustering is an unsupervised machine learning task, we may not have "ground truth" labels at our disposal or do not wish to rely on them. What is needed in such cases are internal measure which permits a label-less analysis of the obtained subspace clustering. In this work, we propose methods for revising clusters obtained from arbitrarily oriented correlation clustering algorithms. Initial experiments conducted reveal improvements in the clustering results compared to the original clustering outcome. Our proposed approach is simple and can be applied as a post-processing step on arbitrarily oriented correlation clusterings. [ABSTRACT FROM AUTHOR]

Blasting performance is influenced by mechanical and structural properties of the rock, on one side, and blast design parameters on the other. This paper describes a new methodology to assess rock mass quality from drill-monitoring data to guide blasting in open pit operations. Principal component analysis has been used to combine measurement while drilling (MWD) information from two drill rigs; corrections of the MWD parameters to minimize external influences other than the rock mass have been applied. First, a Structural factor has been developed to classify the rock condition in three classes (massive, fractured and heavily fractured). From it, a structural block model has been developed to simplify the recognition of rock classes. Video recording of the inner wall of 256 blastholes has been used to calibrate the results obtained. Secondly, a combined strength-grade factor has been obtained based on the analysis of the rock type description and strength properties from geology reports, assaying of drilling chips (ore/waste identification) and 3D unmanned aerial vehicle reconstructions of the post-blast bench face. Data from 302 blastholes, comprised of 26 blasts, have been used for this analysis. From the results, four categories have been identified: soft-waste, hard-waste, transition zone and hard-ore. The model determines zones of soft and hard waste rock (schisted sandstone and limestone, respectively), and hard ore zones (siderite rock type). Finally, the structural block model has been combined with the strength-grade factor in an overall rock factor. This factor, exclusively obtained from drill monitoring data, can provide an automatic assessment of rock structure, strength, and waste/ore identification. [ABSTRACT FROM AUTHOR]

OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapidly changing applications, a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time. [ABSTRACT FROM AUTHOR]

In our project ReMLAV, funded within the DFG Priority Program RATIO (http://www.spp-ratio.de/), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook. [ABSTRACT FROM AUTHOR]

Chains connecting two or more different clusters are a well known problem of clustering algorithms like DBSCAN or Single Linkage Clustering. Since already a small number of points resulting from, e. g., noise can form such a chain and build a bridge between different clusters, it can happen that the results of the clustering algorithm are distorted: several disparate clusters get merged into one. This single-link effect is rather known but to the best of our knowledge there are no satisfying solutions which extract those chains, yet. We present a new algorithm detecting not only straight chains between clusters, but also bent and noisy ones. Users are able to choose between eliminating one dimensional and higher dimensional chains connecting clusters to receive the underlying cluster structure. Also, the desired straightness can be set by the user. As this paper is an extension of [8], we apply our technique not only in combination with DBSCAN but also with single link hierarchical clustering. On a real world dataset containing traffic accidents in Great Britain we were able to detect chains emerging from streets between cities and villages, which led to clusters composed of diverse villages. Additionally, we analyzed the robustness regarding the variance of chains in synthetic experiments. [ABSTRACT FROM AUTHOR]

Correlation clustering detects complex and intricate relationships in high-dimensional data by identifying groups of data points, each characterized by differents correlation among a (sub)set of features. Current correlation clustering methods generally limit themselves to linear correlations only. In this paper, we introduce a method for detecting global non-linear correlated clusters focusing on quadratic relations. We introduce a novel Hough transform for the detection of hyperparaboloids and apply it to the detection of hyperparaboloid correlated clusters in arbitrary high-dimensional data spaces. We further provide a solution for utilizing all available CPU cores on a system. For this we simply split the Hough space among a pre-defined axis into a number of equi-sized partitions. In this paper we show that this most simple way of parallelization already improves the runtime significantly. Non-linear correlation clustering like our method can reveal valuable insights which are not covered by current linear versions. Our empirical results on synthetic and real world data reveal that the proposed method is robust against noise, jitter and irregular densities. [ABSTRACT FROM AUTHOR]

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Measuring the quality of a clustering algorithm has shown to be as important as the algorithm itself. It is a crucial part of choosing the clustering algorithm that performs best for an input data. Streaming input data have many features that make them much more challenging than static ones. They are endless, varying and emerging with high speeds. This raised new challenges for the clustering algorithms as well as for their evaluation measures. Up till now, external evaluation measures were exclusively used for validating stream clustering algorithms. While external validation requires a ground truth which is not provided in most applications, particularly in the streaming case, internal clustering validation is efficient and realistic. In this article, we analyze the properties and performances of eleven internal clustering measures. In particular, we apply these measures to carefully synthesized stream scenarios to reveal how they react to clusterings on evolving data streams using both k-means-based and density-based clustering algorithms. A series of experimental results show that different from the case with static data, the Calinski-Harabasz index performs the best in coping with common aspects and errors of stream clustering for k-means-based algorithms, while the revised validity index performs the best for density-based ones. [ABSTRACT FROM AUTHOR]

Detecting dense subgraphs such as cliques or quasi-cliques is an important graph mining problem. While this task is established for simple graphs, today's applications demand the analysis of more complex graphs: In this work, we consider a frequently observed type of graph where edges represent different types of relations. These multiple edge types can also be viewed as different 'layers' of a graph, which is denoted as a 'multi-layer graph'. Additionally, each edge might be annotated by a label characterizing the given relation in more detail. By simultaneously exploiting all this information, the detection of more interesting subgraphs can be supported. We introduce the multi-layer coherent subgraph model, which defines clusters of vertices that are densely connected by edges with similar labels in a subset of the graph layers. We avoid redundancy in the result by selecting only the most interesting, non-redundant subgraphs for the output. Based on this model, we introduce the best-first search algorithm MiMAG. In thorough experiments, we demonstrate the strengths of MiMAG in comparison with related approaches on synthetic as well as real-world data sets. [ABSTRACT FROM AUTHOR]

Nowadays, most streaming data sources are becoming high dimensional. Accordingly, subspace stream clustering, which aims at finding evolving clusters within subgroups of dimensions, has gained a significant importance. However, in spite of the rich literature of subspace and projected clustering algorithms on static data, only three stream projected algorithms are available. Additionally, existing subspace clustering evaluation measures are mainly designed for static data, and cannot reflect the quality of the evolving nature of data streams. On the other hand, available stream clustering evaluation measures care only about the errors of the full-space clustering but not the quality of subspace clustering. In this article we present a method for designing new stream subspace and projected algorithms. We propose also, to the first of our knowledge, the first subspace clustering measure that is designed for streaming data, called SubCMM: Subspace Cluster Mapping Measure. SubCMM is an effective evaluation measure for stream subspace clustering that is able to handle errors caused by emerging, moving, or splitting subspace clusters. Additionally, we propose a novel method for using available offline subspace clustering measures for data streams over the suggested new algorithms within the Subspace MOA framework. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue including alternative clustering, subspaces and ensemble learning.