Example of International Journal of Business Intelligence and Data Mining format
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Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format
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Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining format Example of International Journal of Business Intelligence and Data Mining 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 Business Intelligence and Data Mining — Template for authors

Categories Rank Trend in last 3 yrs
Statistics, Probability and Uncertainty #121 of 152 down down by 78 ranks
Management Information Systems #91 of 114 down down by 55 ranks
Information Systems and Management #100 of 125 down down by 59 ranks
journal-quality-icon Journal quality:
Low
calendar-icon Last 4 years overview: 160 Published Papers | 123 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/06/2020
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Top papers
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 7.7
SJR: 1.426
SNIP: 1.845
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 8.6
SJR: 0.565
SNIP: 1.611
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 10.5
SJR: 1.564
SNIP: 2.582

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.

0.8

11% from 2019

CiteRatio for International Journal of Business Intelligence and Data Mining from 2016 - 2020
Year Value
2020 0.8
2019 0.9
2018 1.5
2017 1.8
2016 1.0
graph view Graph view
table view Table view

0.178

7% from 2019

SJR for International Journal of Business Intelligence and Data Mining from 2016 - 2020
Year Value
2020 0.178
2019 0.166
2018 0.162
2017 0.249
2016 0.192
graph view Graph view
table view Table view

0.641

69% from 2019

SNIP for International Journal of Business Intelligence and Data Mining from 2016 - 2020
Year Value
2020 0.641
2019 0.379
2018 0.346
2017 1.101
2016 0.455
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 11% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 7% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 69% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

International Journal of Business Intelligence and Data Mining

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Inderscience Publishers

International Journal of Business Intelligence and Data Mining

Approved by publishing and review experts on SciSpace, this template is built as per for International Journal of Business Intelligence and Data Mining formatting guidelines as mentioned in Inderscience Publishers author instructions. The current version was created on 16 Jun 2020 and has been used by 952 authors to write and format their manuscripts to this journal.

i
Last updated on
16 Jun 2020
i
ISSN
1743-8187
i
Impact Factor
Low - 0.365
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
plainnat
i
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

Journal Article DOI: 10.1504/IJBIDM.2005.007318
Support vector machines based on K-means clustering for real-time business intelligence systems
Jiaqi Wang1, Xindong Wu2, Chengqi Zhang1

Abstract:

Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and ... Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KMSVM) algorithm proposed in this paper. The KMSVM algorithm combines the K-means clustering technique with SVM and requires one more input parameter to be determined: the number of clusters. The criterion and strategy to determine the input parameters in the KMSVM algorithm are given in this paper. Experiments compare the KMSVM algorithm with SVM on real-world databases, and the results show that the KMSVM algorithm can speed up the response time of classifiers by both reducing support vectors and maintaining a similar testing accuracy to SVM. read more read less

Topics:

Ranking SVM (65%)65% related to the paper, Support vector machine (56%)56% related to the paper, k-means clustering (55%)55% related to the paper, Cluster analysis (54%)54% related to the paper, Real-time business intelligence (50%)50% related to the paper
133 Citations
Journal Article DOI: 10.1504/IJBIDM.2007.012945
Redundant association rules reduction techniques
Mafruz Zaman Ashrafi1, David Taniar1, Kate A. Smith1

Abstract:

To discover hidden correlations, association rule mining methods use two important constraints known as support and confidence. However, mining methods are often unable to find the best value for these constraints: large number of rules when these thresholds are low; very few rules when these thresholds are high. In addition,... To discover hidden correlations, association rule mining methods use two important constraints known as support and confidence. However, mining methods are often unable to find the best value for these constraints: large number of rules when these thresholds are low; very few rules when these thresholds are high. In addition, regardless of these above thresholds, mining methods produce many rules that have identical meaning or, redundant rules. Indeed such redundant rules seem as a main impediment to efficient utilisation of discovered rules, and should be removed. To achieve this aim, here we present several methods that identify those rules that are redundant and eliminate them. read more read less

Topics:

Association rule learning (57%)57% related to the paper
63 Citations
Journal Article DOI: 10.1504/IJBIDM.2007.016385
Knowledge actionability: satisfying technical and business interestingness
Longbing Cao1, Dan Luo1, Chengqi Zhang1

Abstract:

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significa... Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs. read more read less
57 Citations
Journal Article DOI: 10.1504/IJBIDM.2005.007315
Data mining from 1994 to 2004: an application-orientated review
Sherry Y. Chen1, Xiaohui Liu1

Abstract:

Data mining, which is also known as knowledge discovery, is one of the most popular topics in information technology. It concerns the process of automatically extracting useful information and has the promise of discovering hidden relationships that exist in large databases. These relationships represent valuable knowledge th... Data mining, which is also known as knowledge discovery, is one of the most popular topics in information technology. It concerns the process of automatically extracting useful information and has the promise of discovering hidden relationships that exist in large databases. These relationships represent valuable knowledge that is crucial for many applications. This paper presents a review of works on current applications of data mining, which focus on four main application areas, including bioinformatics data, information retrieval, adaptive hypermedia and electronic commerce. How data mining can enhance functions for these four areas is described. The reader of this paper is expected to get an overview of the state-of-the-art research associated with these applications. Furthermore, we identify the limitations of current works and raise several directions for future research. read more read less

Topics:

Knowledge extraction (62%)62% related to the paper, Adaptive hypermedia (53%)53% related to the paper, Information technology (51%)51% related to the paper, E-commerce (50%)50% related to the paper
55 Citations
open accessOpen access Journal Article DOI: 10.1504/IJBIDM.2006.009135
A unified framework for protecting sensitive association rules in business collaboration

Abstract:

The sharing of association rules has been proven beneficial in business collaboration, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns called sensitive rules. The challenge here is how to protect the sensitive rules without losing the benefit of mining. To ... The sharing of association rules has been proven beneficial in business collaboration, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns called sensitive rules. The challenge here is how to protect the sensitive rules without losing the benefit of mining. To address this problem, we propose a unified framework that combines: a set of algorithms to protect sensitive knowledge; retrieval facilities to speed up the process of knowledge protecting; and a set of metrics to evaluate the effectiveness of the proposed algorithms in terms of information loss and private information disclosure. read more read less

Topics:

Information privacy (55%)55% related to the paper, Association rule learning (51%)51% related to the paper, Private information retrieval (50%)50% related to the paper
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52 Citations
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12. Is International Journal of Business Intelligence and Data Mining's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for International Journal of Business Intelligence and Data Mining?

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 Business Intelligence and Data Mining. 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 Business Intelligence and Data Mining?

The 5 most common citation types in order of usage for International Journal of Business Intelligence and Data Mining are:.

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

15. How do I submit my article to the International Journal of Business Intelligence and Data Mining?

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16. Can I download International Journal of Business Intelligence and Data Mining in Endnote format?

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 Business Intelligence and Data Mining Endnote style according to Elsevier guidelines.

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