Abstract：Data mining topics in the discipline of library and information scienceanalysis of influential terms and Dirichlet multinomial regression topic model Purpose The purpose of this study is to explore to which extent data mining research would be associated with the library and information scienceLISdisciplineThis study aims to identify data mining related subject terms and topics in representative LIS scholarly publicationsDesignmethodologyapproach A large set of bibliographic records over 38000 was collected from a scholarly database representing the fields of LIS and the data miningrespectivelyA multitude of text mining techniques were applied to investigate prevailing subject terms and research topicssuch as influential term analysis and Dirichlet multinomial regression topic modelingFindings The findings of this study revealed the relationship between the LIS and data mining research domainsVarious data mining method terms were observed in recent LIS publicationssuch as machine learningartificial intelligence and neural networksThe topic modeling result identified prevailing data mining related research topics in LISsuch as machine learningdeep learningbig data and among othersIn additionthis study investigated the trends of popular topics in LIS over time in the recent decadeOriginalityvalue This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domainsMultiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysisMethodologicallythe study identified influential terms in each domain using multiple feature selection indicesIn additionDirichlet multinomial regression was applied to explore LIS topics in relation to data miningCollection classification and retrieval and indexesRetrieval and IndexingThe purpose of this research is to explore the degree of correlation between data exploration and research and library and information scienceLISdisciplinesThrough a variety of text exploration technologiessuch as influencing vocabulary analysis and Dirichlet polynomial regression theme models to study topic words and research topics