A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining

D.S.S.K.Dhanalakshmi; Dr. Ch.Suneetha

A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining

D.S.S.K.Dhanalakshmi * §

Contact

Dr. Ch.Suneetha

Contact

§ CMR college of Engineering & Technology

A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining

Article Fingerprint

ReserarchID

CST21FQ5

A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

Abstract

The data generated contemporarily from different communication environments is dynamic in content different from the earlier static data environments. The high speed streams have huge digital data transmitted with rapid context changes unlike static environments where the data is mostly stationery. The process of extracting, classifying, and exploring relevant information from enormous flowing and high speed varying streaming data has several inapplicable issues when static data based strategies are applied. The learning strategies of static data are based on observable and established notion changes for exploring the data whereas in high speed data streams there are no fixed rules or drift strategies existing beforehand and the classification mechanisms have to develop their own learning schemes in terms of the notion changes and Notion Change Acceptance by changing the existing notion, or substituting the existing notion, or creating new notions with evaluation in the classification process in terms of the previous, existing, and the newer incoming notions. The research in this field has devised numerous data stream mining strategies for determining, predicting, and establishing the notion changes in the process of exploring and accurately predicting the next notion change occurrences in Notion Change.

References

31 Cites in Article

Reference Format

J Schlimmer,R Granger (1986). Incremental learning from noisy data.
G Widmer,M Kubat (1996). Learning in the presence of Notion Change and hidden contexts.
J Quinlan (1993). C4.5: programs for machine learning.
Pedro Domingos,Geoff Hulten (2000). Mining high-speed data streams.
Wassily Hoeffding (1963). Probability Inequalities for Sums of Bounded Random Variables.
O Maron,A Moore (1993). Hoeffding races: Accelerating model selection search for classification and function approximation.
C Alippi,M Roveri (2007). Just-in-time adaptive classifiers in non-stationary conditions.
Cesare Alippi,Manuel Roveri (2008). Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier.
K Fukunaga,L Hostetler (2002). Optimization of k nearest neighbor density estimates.
G Carpenter,S Grossberg,N Markuzon,J Reynolds,D Rosen (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps.
Gail Carpenter,Stephen Grossberg,John Reynolds (1991). ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network.
A Andres-Andres,E Gomez-Sanchez,M Botelorenzo (2005). Incremental rule pruning for fuzzy artmap neural network.
Gail Carpenter,Ah-Hwee Tan (1995). Rule Extraction: From Neural Architecture to Symbolic Representation.
Xingquan Zhu,Xindong Wu,Ying Yang Dynamic Classifier Selection for Effective Mining from Noisy Data Streams.
Null- Lee,Cheng-Jung Tsai,Jhe-Hao Wu,Wei-Pang Yang (2007). A Decision Tree-Based Approach to Mining the Rules of Concept Drift.
Yang Zhang,Xue Li (2008). One-class Classification of Text Streams with Notion Change.
H Stephen,Marcus Bach,Maloof (2008). Paired Learners for Notion Change.
Zhenzheng Ouyang,Min Zhou,Tao Wang,Quanyuan Wu (2009). Mining Concept-Drifting and Noisy Data Streams using Ensemble Classifiers.
Peng Zhang,Chuan Zhou,Peng Wang,Byron Gao,Xingquan Zhu,Li Guo (2014). E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams.
Snehlata Kapil Wankhade,Ravindra Dongre,Thool (2012). New Evolving Ensemble Classifier for Handling Notion Changeing Data Streams.
G Marrs,M Black,R Hickey (2012). The use of time stamps in handling latency and concept drift in online learning.
Zohre Karimi,• Hassan,Abolhassani • Hamid Beigy (2012). A new method of incremental mining using harmony search.
Mayank Pal Singh (2013). Quantifying Notion Changeing in Network Traffic using ROC Curves from Naive Bayes Classifiers.
Gansen Zhao,Ziliu Li,Fujiao Liu,Yong Tang (2013). A Concept Drifting Based Clustering Framework for Data Streams.
Muhammad Shaheryar,Mehrosh Khalid,Ali Qamar (2013). Rot-SiLA: A Novel Ensemble Classification Approach Based on Rotation Forest and Similarity Learning Using Nearest Neighbor Algorithm.
Chao-Wei Li,Kuen-Fang Jea (2013). An approach of support approximation to discover frequent patterns from concept-drifting data streams based on concept learning.
Amineh Amini,Member,Teh Ieee,Hadi Ying Wah,Saboohi,Member (2014). On Density-Based Data Streams Clustering Algorithms: A Survey.
M Qamar,E Gaussier,J.-P Chevallet,J Lim (2008). Similarity learning for nearest neighbor classification.
J Rodriguez,L Kuncheva,C Alonso (2006). Rotation forest: A new classifier ensemble method.
M Scholz,R Klinkenberg An ensemble classifier for drifting concepts.
Jin Gao,Wei Fan,Jiawei Han On appropriate assumptions to mine data streams: Analysis and Practice.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

D.S.S.K.Dhanalakshmi, Dr. Ch.Suneetha. 2016. "A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C2).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 16 Issue C2

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification H.2.8

D.3.4

D.2.3

Submission ReceivedDecember 6, 2015
Peer Review Double Blind
Handling Editor
Accepted January 1, 2016
Published January 15, 2016

Version of record

v1.2

Issue date

May 31, 2016

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: D.S.S.K.Dhanalakshmi, Dr. Ch.Suneetha (PhD/Dr. count: 1)

View Count (all-time): 453

Total Views (Real + Logic): 1667

Total Downloads (simulated): 131

Publish Date: 2016 01, Fri

Monthly Totals (Real + Logic):

Month 1: 19 views
Month 2: 53 views
Month 3: 66 views
Month 4: 37 views
Month 5: 36 views
Month 6: 31 views
Month 7: 35 views
Month 8: 26 views
Month 9: 45 views
Month 10: 42 views
Month 11: 17 views
Month 12: 27 views
Month 13: 20 views
Month 14: 23 views
Month 15: 27 views
Month 16: 26 views
Month 17: 26 views
Month 18: 18 views
Month 19: 29 views
Month 20: 28 views
Month 21: 11 views
Month 22: 21 views
Month 23: 29 views
Month 24: 32 views
Month 25: 41 views
Month 26: 23 views
Month 27: 13 views
Month 28: 37 views
Month 29: 31 views
Month 30: 36 views
Month 31: 41 views
Month 32: 42 views
Month 33: 38 views
Month 34: 34 views
Month 35: 20 views
Month 36: 19 views
Month 37: 15 views
Month 38: 21 views
Month 39: 26 views
Month 40: 42 views
Month 41: 40 views
Month 42: 33 views
Month 43: 26 views
Month 44: 33 views
Month 45: 25 views
Month 46: 27 views
Month 47: 29 views
Month 48: 19 views
Month 49: 23 views
Month 50: 39 views
Month 51: 28 views
Month 52: 42 views
Month 53: 28 views
Month 54: 29 views
Month 55: 35 views
Month 56: 34 views
Month 57: 14 views
Month 58: 36 views
Month 59: 42 views
Month 60: 42 views
Month 61: 29 views
Month 62: 35 views
Month 63: 34 views
Month 64: 45 views
Month 65: 39 views
Month 66: 31 views
Month 67: 16 views
Month 68: 14 views
Month 69: 10 views
Month 70: 25 views
Month 71: 33 views
Month 72: 21 views
Month 73: 16 views
Month 74: 32 views
Month 75: 33 views
Month 76: 39 views
Month 77: 31 views
Month 78: 29 views
Month 79: 35 views
Month 80: 35 views
Month 81: 18 views
Month 82: 23 views
Month 83: 42 views
Month 84: 22 views
Month 85: 21 views
Month 86: 29 views
Month 87: 29 views
Month 88: 31 views
Month 89: 21 views
Month 90: 37 views
Month 91: 26 views
Month 92: 37 views
Month 93: 24 views
Month 94: 34 views
Month 95: 29 views
Month 96: 24 views
Month 97: 35 views
Month 98: 29 views
Month 99: 42 views
Month 100: 20 views
Month 101: 13 views
Month 102: 26 views
Month 103: 45 views
Month 104: 40 views
Month 105: 13 views
Month 106: 33 views
Month 107: 20 views
Month 108: 46 views
Month 109: 28 views
Month 110: 21 views
Month 111: 27 views
Month 112: 40 views
Month 113: 27 views
Month 114: 32 views
Month 115: 48 views
Month 116: 46 views
Month 117: 63 views
Month 118: 31 views
Month 119: 54 views
Month 120: 41 views
Month 121: 51 views
Month 122: 16 views
Month 123: 28 views
Month 124: 17 views
Month 125: 23 views
Month 126: 47 views
Month 127: 61 views
Month 128: 36 views
Month 129: 40 views

Total Views: 1.7K

Total Downloads: 131

All Trends