@article{MAKHILLAJIT20141315795,
    title = {Preprocessing and Generation of Association Rules for Bone Marrow Analysis Data of Haematology for Acute Myeloid Leukemia},
    journal = {Asian Journal of Information Technology},
    volume = {13},
    number = {1},
    pages = {29-37},
    year = {2014},
    issn = {1682-3915},
    doi = {ajit.2014.29.37},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2014.29.37},
    author = {D. and},
    keywords = {Association rule mining,bone marrow analysis,haematology,knowledge discovery in databases,blood},
    abstract = {Clinical pathology uses laboratory tests on body fluids such 
  as blood and urine to diagnose diseases. Haematology is the study of blood and 
  blood forming organs such as bone marrow. In this study, researchers analyze 
  the components of the bone marrow and the structure of the bone marrow analysis 
  database. The Knowledge Discovery in Databases (KDD) steps are briefly explained. 
  The 18,000 bone marrow analysis records are collected from a reputed hospital 
  and this raw data is transformed into a preprocessed data using the pre-processing 
  phases of KDD such as data cleaning, data selection and data transformation. 
  Eliminate the tuple technique is used to clean the data. The attributes related 
  to the bone marrow components are selected. The ranges of low, high and normal 
  values for the individual attributes are used to transform the data. The data 
  mining techniques are studied and the Apriori algorithm is selected for finding 
  frequent itemsets that are used for the generation of association rules.}
    }