Multiple Foci Visualisation of Large Hierarchies with FlexTree

2004 ◽  
Vol 3 (1) ◽  
pp. 19-35 ◽  
Author(s):  
Hongzhi Song ◽  
Edwin P. Curran ◽  
Roy Sterritt
Keyword(s):  
Data Mining ◽  
Decision Trees ◽  
Research Effort ◽  
Computer File ◽  
Information Spaces ◽  
Global Context ◽  
Complex Information ◽  
Human Understanding ◽  
Large Hierarchy ◽  
Visualisation Technique

One of the main tasks in information visualisation research is creating visual tools to facilitate human understanding of large and complex information spaces. Hierarchies, being a good mechanism for organising such information, are ubiquitous. Although much research effort has been spent on finding useful representations for hierarchies, visualising large hierarchies is still a difficult topic. One of the difficulties is how to handle the ever increasing scale of hierarchies. Another is how to enable the user to focus on multiple selections of interest while maintaining context. This paper describes a hierarchy visualisation technique called FlexTree to address these problems. It contains some important features that have not been exploited so far. A profile or contour unique to the hierarchy being visualised can be viewed in a bar chart layout. A normalised view of a common attribute of all nodes can be selected by the user. Multiple foci are consistently accessible within a global context through interaction. Furthermore it can handle a large hierarchy that contains 10,000 nodes in a PC environment. This technique has been applied to visualise computer file system structures and decision trees from data mining results. The results from informal user evaluations against these two applications are also presented. User feedback suggests that FlexTree is suitable for visualising large decision trees.

scholarly journals Analisis Rekam Medis Pasien Diabetes Mellitus Melalui Implementasi Teknik Data Mining di RSUP Dr. Sardjito Yogyakarta

2018 ◽  
Vol 2 (2) ◽  
pp. 167
Author(s):  
Marko Ferdian Salim ◽  
Sugeng Sugeng
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Decision Tree ◽  
Decision Trees ◽  
Cross Sectional ◽  
Diabetes Melitus

Latar Belakang: Diabetes mellitus adalah penyakit kronis yang mempengaruhi beban ekonomi dan sosial secara luas. Data pasien dicatat melalui sistem rekam medis pasien yang tersimpan dalam database sistem informasi rumah sakit, data yang tercatat belum dianalisis secara efektif untuk menghasilkan informasi yang berharga. Teknik data mining bisa digunakan untuk menghasilkan informasi yang berharga tersebut.Tujuan: Mengidentifikasi karakteristik pasien Diabetes mellitus, kecenderungan dan tipe Diabetes melitus melalui penerapan teknik data mining di RSUP Dr. Sardjito Yogyakarta.Metode: Penelitian ini merupakan penelitian deskriptif observasional dengan rancangan cross sectional. Teknik pengumpulan data dilakukan secara retrospektif melalui observasi dan studi dokumentasi rekam medis elektronik di RSUP Dr. Sardjito Yogyakarta. Data yang terkumpul kemudian dilakukan analisis dengan menggunakan aplikasi Weka.Hasil: Pasien Diabetes mellitus di RSUP Dr. Sardjito tahun 2011-2016 berjumlah 1.554 orang dengan tren yang cenderung menurun. Pasien paling banyak berusia 56 - 63 tahun (27,86%). Kejadian Diabetes mellitus didominasi oleh Diabetes mellitus tipe 2 dengan komplikasi tertinggi adalah hipertensi, nefropati, dan neuropati. Dengan menggunakan teknik data mining dengan algoritma decision tree J48 (akurasi 88.42%) untuk analisis rekam medis pasien telah menghasilkan beberapa rule.Kesimpulan: Teknik klasifikasi data mining (akurasi 88.42%) dan decision trees telah berhasil mengidentifikasi karakteristik pasien dan menemukan beberapa rules yang dapat digunakan pihak rumah sakit dalam pengambilan keputusan mengenai penyakit Diabetes mellitus.

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 2024-2030
Author(s):  
Malcolm J. Beynonm
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Complex Decision Making ◽  
Fuzzy Decision Trees

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.

Human—Computer Interaction with Global Information Spaces — Beyond Data Mining

2000 ◽  
pp. 32-46 ◽  
Author(s):  
Jim Thomas ◽  
Kris Cook ◽  
Vern Crow ◽  
Beth Hetzler ◽  
Richard May ◽  
...  
Keyword(s):  
Data Mining ◽  
Human Computer Interaction ◽  
Global Information ◽  
Information Spaces ◽  
Computer Interaction

Navigating Complex Information Spaces: A Portfolio Theory Approach

2014 ◽  
pp. 138-144 ◽  
Author(s):  
Payel Bandyopadhyay ◽  
Tuukka Ruotsalo ◽  
Antti Ukkonen ◽  
Giulio Jacucci
Keyword(s):  
Portfolio Theory ◽  
Theory Approach ◽  
Information Spaces ◽  
Complex Information

scholarly journals Towards Interoperability in Genome Databases: The MAtDB (MIPSArabidopsis thalianaDatabase) Experience

2003 ◽  
Vol 4 (2) ◽  
pp. 255-258 ◽  
Author(s):  
Heiko Schoof
Keyword(s):  
Data Mining ◽  
Data Repository ◽  
The Internet ◽  
Distributed Data ◽  
Genome Sequences ◽  
Genome Database ◽  
Complex Information ◽  
Genome Databases ◽  
Application Access ◽  
Mining Tools

Increasing numbers of whole-genome sequences are available, but to interpret them fully requires more than listing all genes. Genome databases are faced with the challenges of integrating heterogenous data and enabling data mining. In comparison to a data warehousing approach, where integration is achieved through replication of all relevant data in a unified schema, distributed approaches provide greater flexibility and maintainability. These are important in a field where new data is generated rapidly and our understanding of the data changes. Interoperability between distributed data sources allows data maintenance to be separated from integration and analysis. Simple ways to access the data can facilitate the development of new data mining tools and the transition from model genome analysis to comparative genomics. With the MIPSArabidopsis thalianagenome database (MAtDB, http://mips.gsf.de/proj/thal/db) our aim is to go beyond a data repository towards creating an integrated knowledge resource. To this end, theArabidopsisgenome has been a backbone against which to structure and integrate heterogenous data. The challenges to be met are continuous updating of data, the design of flexible data models that can evolve with new data, the integration of heterogenous data, e.g. through the use of ontologies, comprehensive views and visualization of complex information, simple interfaces for application access locally or via the Internet, and knowledge transfer across species.

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