Data mining techniques for herbs


  • J Satish Babu
  • M Niveditha
  • V Bhavya
  • K Gowthami



Data Mining, Classification Techniques, Mesh Techniques, Artificial Neural Networks, SOM Algorithm


The most important source of ingredients in the discovery of new drugs are Natural products. Moreover Nagoya protocol is most commonly used in selection of herbs based on similar efficiency, Later scientists have voiced their concern on protocol also proved it as less effective therefore, this project uses data mining classification approaches, novel targeted Selection which makes use of MED - LINE(Medical Literature Analysis and Retrieval system online) database that consists of biomedical information to identify herbs of same efficacy .Neural network technique among all classification techniques is inspired by biological nervous system. AS neural network is successful on wide array of noisy object selection of herbs is done effectively. SOM (self-organizing map) is most popular Neural Network provides a topology preserving mapping from the high dimensional space to map units. The main objective of this project is to survey on various data mining methods and their techniques and to conclude the suitable algorithm.



[1] A. Linda Sherin, D. (2017). SIMILAR HERB SELECTION USING DATA MINING. Ijser.

[2] Aiping Lu, M. J. (n.d.). An integrative approach of linking traditional Chinese medicine pattern classification.

[3] An Encyclopedia of Herb-Disease, a Quick shortcut for herbal research. (n.d.). Jbs.

[4] Arezou Rezaei, A. F. (n.d.). An Encyclopedia of Herb-Disease, a Quick Shortcut for Herbal Research: A Comprehension Based on Iranian Herbal Studies.

[5] Ashish K Sharma, R. K. (n.d.). problems associated with clinical trials of ayurved medicines.

[6] Brendan Coolsaet, T. D. (2013). The Challenges for Implementing the Nagoya Protocol in a Multi-Level Governance Context: Lessons from the Belgian Case. Mdpi.Gori, F. F. (n.d.). Herbal Medicine Today: Clinical and Research Issues.

[7] Jon C Tilburt a, T. J. (n.d.). Herbal medicine research and global health: an ethical analysis.

[8] Juha Vesanto, J. H. (1999). Self-organizing map in MATLAB: the SOM Toolbox.

[9] kasa, T. (2016). Uses of medicinal plants in Ethiopia. Ijar.

[10] Lobo, V. J. (n.d.). Application of Self-Organizing Maps to the Maritime Environment.

[11] Mwasiagi, J. I. (n.d.). Self-Organizing Maps - Applications and Novel Algorithm Design.

[12] P. Venkatesan, M. (n.d.). Visualization of Breast Cancer Data by SOM Component Planes.

[13] Quanquan Gao, T. R. (n.d.). A Classified Herbs Method and Searching Algorithm of Classification Tree Used for Mathematical Measurement of Effect.

[14] Rajdev Tiwari, M. P. (2010). Correlation-based Attribute Selection using Genetic Algorithm. Ijca.

[15] Sang-Jun Yea, B. Y. (2016). A data mining approach to selecting herbs with similar efficacy: Targeted selection methods based on medical subject headings (MesH). Jep.

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