Example of International Journal of Data Mining, Modelling and Management format
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Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format
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Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format Example of International Journal of Data Mining, Modelling and Management format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

International Journal of Data Mining, Modelling and Management — Template for authors

Categories Rank Trend in last 3 yrs
Management Information Systems #86 of 114 down down by 24 ranks
Computer Science Applications #552 of 693 down down by 83 ranks
Modeling and Simulation #245 of 290 down down by 28 ranks
journal-quality-icon Journal quality:
Medium
calendar-icon Last 4 years overview: 71 Published Papers | 69 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 15/07/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.0

29% from 2019

CiteRatio for International Journal of Data Mining, Modelling and Management from 2016 - 2020
Year Value
2020 1.0
2019 1.4
2018 1.1
2017 0.7
2016 0.7
graph view Graph view
table view Table view

0.151

54% from 2019

SJR for International Journal of Data Mining, Modelling and Management from 2016 - 2020
Year Value
2020 0.151
2019 0.331
2018 0.264
2017 0.209
2016 0.178
graph view Graph view
table view Table view

0.372

63% from 2019

SNIP for International Journal of Data Mining, Modelling and Management from 2016 - 2020
Year Value
2020 0.372
2019 1.018
2018 0.849
2017 0.443
2016 0.611
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 29% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 54% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 63% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
International Journal of Data Mining, Modelling and Management

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Inderscience Publishers

International Journal of Data Mining, Modelling and Management

Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts....... Read More

i
Last updated on
15 Jul 2020
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ISSN
1759-1163
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Impact Factor
Medium - 0.574
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Acceptance Rate
Not provided
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Frequency
Not provided
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Open Access
No
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Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
plainnat
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
Beenakker, C. W. J. (2006). ‘Specular Andreev Reflection in Graphene’. Phys. Rev. Lett., Vol 97, No 6, pp. 067007.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1504/IJDMMM.2008.022537
Is an ordinal class structure useful in classifier learning
Jens Christian Hühn1, Eyke Hüllermeier1

Abstract:

In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected i... In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. The purpose of this paper is to investigate, on an experimental basis, the validity of this assumption. Moreover, we seek to answer the question to what extent existing techniques and learning algorithms for ordinal classification are able to exploit order information and which properties of these techniques are important in this regard. read more read less

Topics:

Instance-based learning (60%)60% related to the paper, Statistical classification (59%)59% related to the paper, Learning classifier system (58%)58% related to the paper
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57 Citations
Journal Article DOI: 10.1504/IJDMMM.2009.026076
Privacy preserving record linkage approaches
Vassilios S. Verykios1, Alexandros Karakasidis1, Vassilios K. Mitrogiannis

Abstract:

Privacy-preserving record linkage is a very important task, mostly because of the very sensitive nature of the personal data. The main focus in this task is to find a way to match records from among different organisation data sets or databases without revealing competitive or personal information to non-owners. Towards accom... Privacy-preserving record linkage is a very important task, mostly because of the very sensitive nature of the personal data. The main focus in this task is to find a way to match records from among different organisation data sets or databases without revealing competitive or personal information to non-owners. Towards accomplishing this task, several methods and protocols have been proposed. In this work, we propose a certain methodology for preserving the privacy of various record linkage approaches and we implement, examine and compare four pairs of privacy preserving record linkage methods and protocols. Two of these protocols use n-gram based similarity comparison techniques, the third protocol uses the well known edit distance and the fourth one implements the Jaro-Winkler distance metric. All of the protocols used are enhanced by private key cryptography and hash encoding. This paper presents also a blocking scheme as an extension to the privacy preserving record linkage methodology. Our comparison is backed up by extended experimental evaluation that demonstrates the performance achieved by each of the proposed protocols. read more read less

Topics:

Privacy software (56%)56% related to the paper, Hash function (52%)52% related to the paper, Record linkage (52%)52% related to the paper, Personally identifiable information (51%)51% related to the paper, Cryptography (51%)51% related to the paper
39 Citations
open accessOpen access Journal Article DOI: 10.1504/IJDMMM.2008.022538
Mining event histories: a social science perspective
Gilbert Ritschard1, Alexis Gabadinho1, Nicolas S. Müller1, Matthias Studer1

Abstract:

We explore how recent data mining-based tools developed in domains such as biomedicine or text mining for extracting interesting knowledge from sequence data could be applied to personal life course data. We focus on two types of approaches: 'survival' trees that attempt to partition the data into homogeneous groups regarding... We explore how recent data mining-based tools developed in domains such as biomedicine or text mining for extracting interesting knowledge from sequence data could be applied to personal life course data. We focus on two types of approaches: 'survival' trees that attempt to partition the data into homogeneous groups regarding their survival characteristics, i.e., the duration until a given event occurs and the mining of typical discriminating episodes. We show how these approaches may fruitfully complement the outcome of more classical event history analyses and single out some specific issues raised by their application to socio-demographic data. read more read less
View PDF
30 Citations
Journal Article DOI: 10.1504/IJDMMM.2014.066762
Using trajectories for collaborative filtering-based POI recommendation
Haosheng Huang1, Georg Gartner1

Abstract:

Current mobile guides often suffer from the following problems: a long knowledge acquisition process of recommending relevant points of interest (POIs), the lack of social navigation support, and the challenge of making implicit user-generated content (e.g., trajectories) useful. Collaborative filtering (CF) is a promising so... Current mobile guides often suffer from the following problems: a long knowledge acquisition process of recommending relevant points of interest (POIs), the lack of social navigation support, and the challenge of making implicit user-generated content (e.g., trajectories) useful. Collaborative filtering (CF) is a promising solution for these problems. This article employs CF to mine GPS trajectories for providing Amazon-like POI recommendations. Three CF methods are designed: simple_CF, freq_CF (considering visit frequencies of POIs), and freq_seq_CF (considering both user’s preferences and spatio-temporal behaviour). With these, services like “after visiting …, people similar to you often went to …” can be provided. The methods are evaluated with two GPS datasets. The results show that the CF methods can provide more accurate predictions than simple location-based methods. Also considering visit frequencies (popularity) of POIs and spatio-temporal motion behaviour (mainly the ways in which POIs are visited) in CF can improve the predictive performance. read more read less

Topics:

Collaborative filtering (51%)51% related to the paper
28 Citations
open accessOpen access Journal Article DOI: 10.1504/IJDMMM.2008.022540
A relational perspective on spatial data mining
Donato Malerba1

Abstract:

Remote sensing and mobile devices nowadays collect a huge amount of spatial data, which have to be analysed in order to discover interesting information about economic, social and scientific problems. However, the presence of a spatial dimension adds some problems to data mining tasks. The geometrical representation and relat... Remote sensing and mobile devices nowadays collect a huge amount of spatial data, which have to be analysed in order to discover interesting information about economic, social and scientific problems. However, the presence of a spatial dimension adds some problems to data mining tasks. The geometrical representation and relative positioning of spatial objects implicitly define spatial relationships, whose efficient computation requires a tight integration of the data mining system with the spatial DBMS. The interaction between spatially close objects causes different forms of autocorrelation, whose effect should be considered to improve the predictive accuracy of induced models and patterns. Units of analysis are typically composed of several spatial objects with different properties and their structure cannot be easily accommodated by classical double entry tabular data. In the paper, a way is shown to face these problems when a (multi-)relational data mining approach is considered for spatial data analy... read more read less

Topics:

Object-based spatial database (63%)63% related to the paper, Relational data mining (63%)63% related to the paper, Spatial database (63%)63% related to the paper, Spatial analysis (60%)60% related to the paper, Data stream mining (59%)59% related to the paper
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27 Citations
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International Journal of Data Mining, Modelling and Management format uses plainnat citation style.

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3. Can I cite my article in multiple styles in International Journal of Data Mining, Modelling and Management?

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12. Is International Journal of Data Mining, Modelling and Management's impact factor high enough that I should try publishing my article there?

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13. What is Sherpa RoMEO Archiving Policy for International Journal of Data Mining, Modelling and Management?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for International Journal of Data Mining, Modelling and Management. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In International Journal of Data Mining, Modelling and Management?

The 5 most common citation types in order of usage for International Journal of Data Mining, Modelling and Management are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in International Journal of Data Mining, Modelling and Management Endnote style according to Elsevier guidelines.

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