scholarly journals SAES: An Introduction to Self-Adapting Exploratory Structures

2019 ◽  
Vol 11 (3) ◽  
pp. 54
Author(s):  
Giovanni Sacco
Keyword(s):  
Exploratory Search ◽  
Dynamic Clustering ◽  
Clustering Methods ◽  
Faceted Search ◽  
Information Base ◽  
Single Interface ◽  
Tag Clouds

Self-adapting exploratory structures (SAESs) are the basic components of exploratory search. They are abstract structures which allow searching or querying of an information base and summarizing of results using a uniform representation. A definition and a characterization of SAES is given, as well as a discussion of structures that are SAES or can be modified in order to become SAES. These include dynamic taxonomies (also known as faceted search), tag clouds, continuous sliders, geographic maps, and dynamic clustering methods, such as Scatter-Gather. Finally, the integration of these structures into a single interface is discussed.

scholarly journals Creating Sparks: Comparing Search Results Using Discriminatory Search Term Word Co-Occurrence to Facilitate Serendipity in the Enterprise

2015 ◽  
Vol 14 (01) ◽  
pp. 1550007 ◽  
Author(s):  
Paul Hugh Cleverley ◽  
Simon Burnett
Keyword(s):  
User Interface ◽  
Knowledge Discovery ◽  
Interface Design ◽  
Organisational Learning ◽  
Search Term ◽  
Exploratory Search ◽  
Faceted Search ◽  
Search Results ◽  
Information Item ◽  
Colour Coding

Categories or tags that appear in faceted search interfaces which are representative of an information item, rarely convey unexpected or non-obvious associated concepts buried within search results. No prior research has been identified which assesses the usefulness of discriminative search term word co-occurrence to generate facets to act as catalysts to facilitate insightful and serendipitous encounters during exploratory search. In this study, 53 scientists from two organisations interacted with semi-interactive stimuli, 74% expressing a large/moderate desire to use such techniques within their workplace. Preferences were shown for certain algorithms and colour coding. Insightful and serendipitous encounters were identified. These techniques appear to offer a significant improvement over existing approaches used within the study organisations, providing further evidence that insightful and serendipitous encounters can be facilitated in the search user interface. This research has implications for organisational learning, knowledge discovery and exploratory search interface design.

scholarly journals A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Devotha G. Nyambo ◽  
Edith T. Luhanga ◽  
Zaipuna Q. Yonah
Keyword(s):  
Smallholder Farmers ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Machine Learning Algorithms ◽  
Research Articles ◽  
Clustering Methods ◽  
Training Models ◽  
Testing Dataset ◽  
Farm Types

Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models’ robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables.

scholarly journals Unsupervised Two-Way Clustering of Metagenomic Sequences

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Shruthi Prabhakara ◽  
Raj Acharya
Keyword(s):  
Microbial Community ◽  
Mixture Model ◽  
High Dimensionality ◽  
Read Length ◽  
Clustering Methods ◽  
Mixture Of Gaussians ◽  
Proposed Model ◽  
A Genome ◽  
Relative Abundances

A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.

scholarly journals Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

2021 ◽  
Author(s):  
Polina Suter ◽  
Eva Dazert ◽  
Jack Kuipers ◽  
Charlotte K.Y. Ng ◽  
Tuyana Boldanova ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Bayesian Network ◽  
Molecular Characterization ◽  
Mixture Model ◽  
Clinical Stage ◽  
Molecular Characteristics ◽  
Clustering Methods ◽  
Cancer Subtypes ◽  
Molecular Phenotypes

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

scholarly journals Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5848
Author(s):  
Vadim Borisov ◽  
Maksim Dli ◽  
Artem Vasiliev ◽  
Yaroslav Fedulov ◽  
Elena Kirillova ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Cognitive Modeling ◽  
Cluster Structure ◽  
Energy Systems ◽  
Energy System ◽  
Dynamic Clustering ◽  
Clustering Methods ◽  
Cognitive Approach ◽  
Combined Use ◽  
The Stability

A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.

Facet-value extraction scheme from textual contents in XML data

2015 ◽  
Vol 11 (3) ◽  
pp. 270-290
Author(s):  
Takahiro Komamizu ◽  
Toshiyuki Amagasa ◽  
Hiroyuki Kitagawa
Keyword(s):  
Design Methodology ◽  
The Other ◽  
Exploratory Search ◽  
Xml Data ◽  
Content Type ◽  
Faceted Search ◽  
Search Results ◽  
Extraction Scheme ◽  
High Level ◽  
The Way

Purpose – The purpose of this paper is to extract appropriate terms to summarize the current results in terms of the contents of textual facets. Faceted search on XML data helps users find necessary information from XML data by giving attribute–content pairs (called facet-value pair) about the current search results. However, if most of the contents of a facet have longer texts in average (such facets are called textual facets), it is not easy to overview the current results. Design/methodology/approach – The proposed approach is based upon subsumption relationships of terms among the contents of a facet. The subsumption relationship can be extracted using co-occurrences of terms among a number of documents (in this paper, a content of a facet is considered as a document). Subsumption relationships compose hierarchies, and the authors utilize the hierarchies to extract facet-values from textual facets. In the faceted search context, users have ambiguous search demands, they expect broader terms. Thus, we extract high-level terms in the hierarchies as facet-values. Findings – The main findings of this paper are the extracted terms improve users’ search experiences, especially in cases when the search demands are ambiguous. Originality/value – An originality of this paper is the way to utilize the textual contents of XML data for improving users’ search experiences on faceted search. The other originality is how to design the tasks to evaluate exploratory search like faceted search.

scholarly journals Scanning Impedance Microscopy: From Impedance Spectra to Impedance Images

2001 ◽  
Vol 699 ◽  
Author(s):  
Sergei V. Kalinin ◽  
Dawn A. Bonnell
Keyword(s):  
Transport Properties ◽  
Quantitative Imaging ◽  
Polycrystalline Materials ◽  
Impedance Spectra ◽  
Inhomogeneous Materials ◽  
Single Interface ◽  
Microscopy Technique ◽  
Voltage Phase ◽  
Scanning Surface Potential Microscopy

Abstract:Impedance spectroscopy has long been recognized as one of the major techniques for the characterization of ac transport in materials. The primary limitation of this technique is the lack of spatial resolution that precludes the equivalent circuit elements from being unambiguously associated with individual microstructural features. Here we present a scanning probe microscopy technique for quantitative imaging of ac and dc transport properties of electrically inhomogeneous materials. This technique, referred to as Scanning Impedance Microscopy (SIM), maps the phase and amplitude of local potential with respect to an electric field applied across the sample. Amplitude and phase behavior of individual defects can be correlated with their transport properties. The frequency dependence of the voltage phase shift across an interface yields capacitance and resistance. SIM of single interfaces is demonstrated on a model metal-semiconductor junction. The local interface capacitance and resistance obtained from SIM measurements agrees quantitatively with macroscopic impedance spectroscopy. Superposition of a dc sample bias during SIM probes the C-V characteristics of the interface. When combined with Scanning Surface Potential Microscopy (SSPM), which can be used to determine interface I-V characteristic, local transport properties are completely determined. SIM and SSPM of polycrystalline materials are demonstrated on BiFeO3 and p-doped silicon. An excellent agreement between the properties of a single interface determined by SIM and traditional impedance spectra is demonstrated. Finally, the applicability of this technique for imaging transport behavior in nanoelectronic devices is illustrated with carbon nanotube circuit.

scholarly journals Monophyletic clustering and characterization of protein families

2007 ◽  
Vol 4 (3) ◽  
pp. 89-100 ◽  
Author(s):  
Jian Zhang ◽  
Zhiyuan Zhao ◽  
Jennifer Evershed ◽  
Guoying Li
Keyword(s):  
Software Package ◽  
Sequence Similarity ◽  
Correlation Coefficients ◽  
Protein Family ◽  
Clustering Methods ◽  
Clustering Method ◽  
Protein Families ◽  
Sample Mean ◽  
Mean And Variance

Summary A protein family contains sequences that are evolutionarily related. Generally, this is reflected by sequence similarity. There have been many attempts to organize the set of protein families into evolutionarily homogenous clusters using certain clustering methods. How do we characterize these clusters? How can we cluster protein families using these characterizations? In this work, these questions were addressed by use of a concept called group-wide co-evolution, and was exemplified by some real and simulated protein family data. The results have shown that the trend of a group of monophyletic proteins might be characterized by a normal distribution, while the strength and variability of this trend can be described by the sample mean and variance of the observed correlation coefficients after a suitable transformation. To exploit this property, we have developed a monophyletic clustering method called monophyletic k−medoids clustering. A software package written in R has been made available at http://www.kent.ac.uk/ims/personal/jz .

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