Open AccessA survey for acquiring frequent and sequential items in E-commerce sites
Author(s) -
P Haritha,
Sree Devi M,
K Ravali,
Manoj Pruthvi M
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.1.9484
Subject(s) - association rule learning , computer science , sequential pattern mining , data mining , sequence (biology) , order (exchange) , information overload , e commerce , customer satisfaction , data science , information retrieval , world wide web , marketing , business , finance , genetics , biology
Large amounts of data has made available because of the increase in e-commerce industry. Data has high significance and also important for everyone. Hundreds of websites are being deployed and each site offers millions of products. In addition to this there are several types of input forms. Different sites have different input item collection. This means that there is a substantial amount of information being provided resulting in information overload and in turn results in reduced customer satisfaction and interest. This huge amount of data needs to get processed so that we can able to extract the useful information. From this useful information we can able to increase customer interest, satisfaction along with sales of e-commerce sites. Presenting frequent and sequential patterns in e-commerce sites results in increase of sales of products without delay. Different association rule mining techniques and sequential rule mining techniques can be used for different sets of input forms in order to generate frequent and sequential patterns. This paper discusses various algorithms using techniques such as association rule mining, sequence rule mining proposed for mining frequent and sequential items.