Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)

Mojahed, Aalaa; Bettencourt‐Silva, Joao H.; Wang, Wenjia; Iglesia, Beatriz de la

doi:10.1007/978-3-319-21024-7_17

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…Aalaa et al [10] applied the machine learning clustering method on heterogeneous (not necessarily multimedia though) datasets. Due to the fact that there were not many heterogeneous datasets publicly available, they created their own heterogeneous datasets, which contained different types of media.…”

Section: Related Workmentioning

confidence: 99%

Feature Level Ensemble Method for Classifying Multi-media Data

Alyahyan

Wang

2017

Artificial Intelligence XXXIV

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Abstract. Multimedia data consists of several different types of data, such as numbers, text, images, audio etc. and they usually need to be fused or integrated before analysis. This study investigates a feature-level aggregation approach to combine multimedia datasets for building heterogeneous ensembles for classification. It firstly aggregates multimedia datasets at feature level to form a normalised big dataset, then uses some parts of it to generate classifiers with different learning algorithms. Finally it applies three rules to select appropriate classifiers based on their accuracy and/or diversity to build heterogeneous ensembles. The method is tested on a multimedia dataset and the results show that the heterogeneous ensembles outperform the individual classifiers as well as homogeneous ensembles. However, it should be noted that, it is possible on some cases that the combined dataset does not produce better results than using single media data.

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Section: Related Workmentioning

confidence: 99%

Feature Level Ensemble Method for Classifying Multi-media Data

Alyahyan

Wang

2017

Artificial Intelligence XXXIV

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“…Additional analysis of the shape of the curves represented (for example, clustering of biomarker trends) is also possible using this framework and some work has already been done in this area using fusion methods [57]. …”

Section: Knowledge Discovery Supportmentioning

confidence: 99%

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Bettencourt‐Silva

Mannu

Iglesia

2016

Lecture Notes in Computer Science

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Abstract. Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.

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“…Important notation used in this paper from this point is summarised in Table 1. A definition of our problem has been given in (Mojahed and De La Iglesia, 2014;Mojahed et al, 2015) but we reproduce it here to aid the reader in following the discussion. The formal definition of a heterogeneous dataset, H, is a set of objects such that H = {O 1 , O 2 , .…”

Section: Problem Statementmentioning

confidence: 99%

“…For this grouping we used death indicators after conducting some changes on the values of the corresponding attribute in the data preparation stage (for details see Mojahed et al (2015)). …”

Section: -Mortality Groupingmentioning

confidence: 99%

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

Mojahed

Iglesia

2016

Knowl Inf Syst

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Abstract. This paper introduces Hk -medoids, a modified version of the standard kmedoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk -medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k -medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation.

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Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)

Cited by 9 publications

References 15 publications

Feature Level Ensemble Method for Classifying Multi-media Data

Feature Level Ensemble Method for Classifying Multi-media Data

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

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