Convergence of knowledge, nature and computations: a review

Pandey, Subhash C.; Nandi, Gora Chand

doi:10.1007/s00500-014-1510-7

Cited by 9 publications

(2 citation statements)

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“…NC commonly involves developing algorithms that draw inspiration from the natural world, using natural material as computational media, and simulating natural phenomena in computers (Brabazon et al, 2015). For example, biological phenomena such as evolution, ecology, and swarm inspire optimization algorithms (Pandey and Nandi, 2016). Genetic algorithms (GA) are the most popular instance of evolutionary computation (EC) (Drugan, 2019).…”

Section: Main Themes In Ai For Sustainability Researchmentioning

confidence: 99%

Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

Nishant

Kennedy

Corbett

2020

International Journal of Information Management

459

126

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Section: Main Themes In Ai For Sustainability Researchmentioning

confidence: 99%

Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

Nishant

Kennedy

Corbett

2020

International Journal of Information Management

459

126

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“…Data mining, an analytical tool, helps to solve this problem as it is the process of extracting hidden, interesting, nontrivial, potentially useful and previously unknown information from large databases. Ideally, data mining process should be capable of extracting (semi‐)automatically knowledge from the data, but it is also important to think what kind of knowledge we should try to discover (Pandey & Nandi, ). Data mining techniques can be grouped on the basis of knowledge to be mined.…”

Section: Introductionmentioning

confidence: 99%

Clustering approaches for high‐dimensional databases: A review

Mittal¹,

Goyal

Hemanth

et al. 2019

WIREs Data Min & Knowl

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Data mining is an inevitable task in most of the emerging computing technologies as it debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the efficacy to envisage ingeniously the vast and heterogeneous datasets and thus delineates substantial knowledge from the abundance of data by pragmatic implementation of suitable algorithm. There are galore of algorithms in literature for this purpose. Furthermore, clustering is widely used techniques to analyze the data within the purview of data mining and thus it became as a motivational impetus for the authors to survey the existing literature on this topic rigorously and have consequently identified various key parameters so that concomitant improvement can be possible while selecting a best fit clustering algorithm pertaining to a specific problem domain. Furthermore, clustering, classification and association rule mining are akin and indispensable to data mining and owing to these authors have also included interrelation and intertwining among these terms so that this work will presage chunk of help for the researchers working in this field. The present study also envisages and manifests the challenges associated with the clustering algorithms for two‐ and high‐dimensional databases in a flamboyant fashion. Over and above, this work identifies key parametric attributes to assess the clustering algorithms which in turn benevolent the existing work and paves the way for profound future research in this realm. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Classification Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Big Data Mining

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