Journal of Applied Information Science

S. Vijayarani ¹

Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, IN

Abstract

The concept of sequence data mining aimed to retrieve the frequent patterns in the sequences of products purchased by the customers through the time ordered transactions. Later, the application of sequence mining was extended to complex applications like telecommunication, network detection, DNA and protein sequence research. The technique of searching among sequence data is very important in many applications. A search technique is a technique for finding an item with specified properties among a collection of items. The searching process in sequence databases plays an important role in many application domains, mainly for information retrieval and data mining. When there are a number of stored objects, it will be too slow to linearly search all the stored items to find those that satisfy the query criteria. Hence various techniques and data structures are required to organise and manage the search process so that objects relevant to the query can be located quickly. In this research work, a new sequence search technique SSPP is proposed for performing sequence search operation in a retail dataset. The performance of SSPP technique is analysed to show the efficiency of the proposed technique when compared to other sequence search methods.

Keywords

Sequence, Sequence Database, Prefix Span, SSP, SSPP.

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Sivamathi Chokkalingam ¹, S. Vijayarani ²

Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, Tamil Nadu, IN

Abstract

Utility mining is an emerging topic in data mining. The aim of utility mining is to discover the itemsets that have maximum utilities. Here utility refers number of items bought, cost of an item or it can be any other user choice in a transaction database. Frequent itemset mining is starting point of utility mining. In frequent itemset mining most often occurring itemsets in a transaction are retrieved. The discovery of such frequent itemsets can help in many business decision making process. Frequent itemset mining concentrates on the number of occurrence of items in a transaction, but not the value of items. But utility mining considers importance of itemsets like the profit it earns in a transaction, quantity in a transaction. In this paper various utility mining algorithms like MEU (Mining with expected utility), FUM (Fast Utility Mining), Two-Phase, CTU-Mine, UP-Growth (Utility Pattern Growth), and FHM (Faster High Utility itemset Mining) MHUI-BIT (Mining High-Utility Itemsets based on BIT vector), MHUT-TID (Mining High-Utility Itemsets based on TIDlist), and THUI (Temporal High Utility Itemsets) are discussed.

Keywords

Utility Mining, High Utility Itemset Mining, MEU, MHUI-BIT & MHUT-TID, THUI-Mine, FUM, Two-Phase CTU-Mine, UP-Growth, FHM.

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