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024 7 _a10.1007/978-981-16-3964-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aPeriodic Pattern Mining
_h[electronic resource] :
_bTheory, Algorithms, and Applications /
_cedited by R. Uday Kiran, Philippe Fournier-Viger, Jose M. Luna, Jerry Chun-Wei Lin, Anirban Mondal.
250 _a1st ed. 2021.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2021.
300 _aVIII, 263 p. 65 illus., 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Introduction to Data Mining -- Chapter 2: Discovering Frequent Patterns in Very Large Transactional Database -- Chapter 3: Discovering Periodic Frequent Patterns in Temporal Databases -- Chapter 4: Discovering Fuzzy Periodic Frequent Patterns in Quantitative Temporal Databases -- Chapter 5: Discovering Partial Periodic Patterns in Temporal Databases -- Chapter 6: Finding Periodic Patterns in Multiple Sequences -- Chapter 7: Discovering Self Reliant Patterns -- Chapter 8: Finding Periodic High Utility Patterns in Sequence -- Chapter 9: Mining Periodic High Utility Sequential Patterns with Negative Unit Profits -- Chapter 10: Hiding Periodic High Utility Sequential Patterns -- Chapter 11: NetHAPP -- Chapter 12: Privacy Preservation of Periodic Frequent Patterns using Sensitive Inverse Frequency.
520 _aThis book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 0 _aData mining.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aKiran, R. Uday.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aFournier-Viger, Philippe.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLuna, Jose M.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLin, Jerry Chun-Wei.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMondal, Anirban.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811639630
776 0 8 _iPrinted edition:
_z9789811639654
776 0 8 _iPrinted edition:
_z9789811639661
856 4 0 _uhttps://doi.org/10.1007/978-981-16-3964-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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