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Data mining environment produces a large amount of data, that need to be analyzed, patterns have to be extracted from that to gain knowledge. In this new era with boom of data both structured and unstructured, in the field of genomics, meteorology, biology, environmental research and many others, it has become difficult to process, manage and analyze patterns using traditional databases and architectures. So, a proper architecture should be understood to gain knowledge about the Big Data. This paper presents a review of various algorithms from 1994-2013 necessary for handling such large data set. These algorithms define various structures and methods implemented to handle Big Data, also in the paper are listed various tool that were developed for analyzing them.
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Stored only when both papers are in the corpus — 53 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.