323,455 research outputs found
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.Comment: The MATLAB source code of this work is available at:
https://www.researchgate.net/publication/28197031
Multi-Source Multi-View Clustering via Discrepancy Penalty
With the advance of technology, entities can be observed in multiple views.
Multiple views containing different types of features can be used for
clustering. Although multi-view clustering has been successfully applied in
many applications, the previous methods usually assume the complete instance
mapping between different views. In many real-world applications, information
can be gathered from multiple sources, while each source can contain multiple
views, which are more cohesive for learning. The views under the same source
are usually fully mapped, but they can be very heterogeneous. Moreover, the
mappings between different sources are usually incomplete and partially
observed, which makes it more difficult to integrate all the views across
different sources. In this paper, we propose MMC (Multi-source Multi-view
Clustering), which is a framework based on collective spectral clustering with
a discrepancy penalty across sources, to tackle these challenges. MMC has
several advantages compared with other existing methods. First, MMC can deal
with incomplete mapping between sources. Second, it considers the disagreements
between sources while treating views in the same source as a cohesive set.
Third, MMC also tries to infer the instance similarities across sources to
enhance the clustering performance. Extensive experiments conducted on
real-world data demonstrate the effectiveness of the proposed approach
Modeling and estimation of multi-source clustering in crime and security data
While the presence of clustering in crime and security event data is well
established, the mechanism(s) by which clustering arises is not fully
understood. Both contagion models and history independent correlation models
are applied, but not simultaneously. In an attempt to disentangle contagion
from other types of correlation, we consider a Hawkes process with background
rate driven by a log Gaussian Cox process. Our inference methodology is an
efficient Metropolis adjusted Langevin algorithm for filtering of the intensity
and estimation of the model parameters. We apply the methodology to property
and violent crime data from Chicago, terrorist attack data from Northern
Ireland and Israel, and civilian casualty data from Iraq. For each data set we
quantify the uncertainty in the levels of contagion vs. history independent
correlation.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS647 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The interaction of normalisation and clustering in sub-domain definition for multi-source transfer learning based time series anomaly detection
This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the target data can result in improved model performance, as compared to identifying sub-domains through defining clusters using the multi-source dataset
Distributed Holistic Clustering on Linked Data
Link discovery is an active field of research to support data integration in
the Web of Data. Due to the huge size and number of available data sources,
efficient and effective link discovery is a very challenging task. Common
pairwise link discovery approaches do not scale to many sources with very large
entity sets. We here propose a distributed holistic approach to link many data
sources based on a clustering of entities that represent the same real-world
object. Our clustering approach provides a compact and fused representation of
entities, and can identify errors in existing links as well as many new links.
We support a distributed execution of the clustering approach to achieve faster
execution times and scalability for large real-world data sets. We provide a
novel gold standard for multi-source clustering, and evaluate our methods with
respect to effectiveness and efficiency for large data sets from the geographic
and music domains
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
This paper presents a novel pairwise constraint propagation approach by
decomposing the challenging constraint propagation problem into a set of
independent semi-supervised learning subproblems which can be solved in
quadratic time using label propagation based on k-nearest neighbor graphs.
Considering that this time cost is proportional to the number of all possible
pairwise constraints, our approach actually provides an efficient solution for
exhaustively propagating pairwise constraints throughout the entire dataset.
The resulting exhaustive set of propagated pairwise constraints are further
used to adjust the similarity matrix for constrained spectral clustering. Other
than the traditional constraint propagation on single-source data, our approach
is also extended to more challenging constraint propagation on multi-source
data where each pairwise constraint is defined over a pair of data points from
different sources. This multi-source constraint propagation has an important
application to cross-modal multimedia retrieval. Extensive results have shown
the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201
Clustering Approaches for Multi-source Entity Resolution
Entity Resolution (ER) or deduplication aims at identifying entities, such as specific customer or product descriptions, in one or several data sources that refer to the same real-world entity. ER is of key importance for improving data quality and has a crucial role in data integration and querying. The previous generation of ER approaches focus on integrating records from two relational databases or performing deduplication within a single database. Nevertheless, in the era of Big Data the number of available data sources is increasing rapidly. Therefore, large-scale data mining or querying systems need to integrate data obtained from numerous sources. For example, in online digital libraries or E-Shops, publications or products are incorporated from a large number of archives or suppliers across the world or within a specified region or country to provide a unified view for the user. This process requires data consolidation from numerous heterogeneous data sources, which are mostly evolving. By raising the number of sources, data heterogeneity and velocity as well as the variance in data quality is increased. Therefore, multi-source ER, i.e. finding matching entities in an arbitrary number of sources, is a challenging task. Previous efforts for matching and clustering entities between multiple sources (> 2) mostly treated all sources as a single source. This approach excludes utilizing metadata or provenance information for enhancing the integration quality and leads up to poor results due to ignorance of the discrepancy between quality of sources.
The conventional ER pipeline consists of blocking, pair-wise matching of entities, and classification. In order to meet the new needs and requirements, holistic clustering approaches that are capable of scaling to many data sources are needed. The holistic clustering-based ER should further overcome the restriction of pairwise linking of entities by making the process capable of grouping entities from multiple sources into clusters. The clustering step aims at removing false links while adding missing true links across sources. Additionally, incremental clustering and repairing approaches need to be developed to cope with the ever-increasing number of sources and new incoming entities.
To this end, we developed novel clustering and repairing schemes for multi-source entity resolution. The approaches are capable of grouping entities from multiple clean (duplicate-free) sources, as well as handling data from an arbitrary combination of clean and dirty sources. The multi-source clustering schemes exclusively developed for multi-source ER can obtain superior results compared to general purpose clustering algorithms. Additionally, we developed incremental clustering and repairing methods in order to handle the evolving sources. The proposed incremental approaches are capable of incorporating new sources as well as new entities from existing sources. The more sophisticated approach is able to repair previously determined clusters, and consequently yields improved quality and a reduced dependency on the insert order of the new entities.
To ensure scalability, the parallel variation of all approaches are implemented on top of the Apache Flink framework which is a distributed processing engine. The proposed methods have been integrated in a new end-to-end ER tool named FAMER (FAst Multi-source Entity Resolution system). The FAMER framework is comprised of Linking and Clustering components encompassing both batch and incremental ER functionalities. The output of Linking part is recorded as a similarity graph where each vertex represents an entity and each edge maintains the similarity relationship between two entities. Such a similarity graph is the input of the Clustering component. The comprehensive comparative evaluations overall show that the proposed clustering and repairing approaches for both batch and incremental ER achieve high quality while maintaining the scalability
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