Discovering interesting patterns and useful knowledge from massive data has become an important data mining task.
These days, we come across a lot of things that have profit technically referred as external utility, value greater than the other item
sets in the database. Utility mining is an important topic in data mining and has received extensive research in last few years. In
utility mining, each item is associated with a utility that could be profit, quantity, cost or other user preferences. Objective of Utility
Mining is to identify the item sets with highest utilities. High utility itemset mining is an extension to the problem of frequent pattern
mining. Many algorithms have been proposed in this field in the recent years. In this paper we emphasis on an emerging area called
High Utility Mining which not only considers the frequency of the itemsets but also considers the utility associated with the itemsets.
In High Utility Itemset Mining the target is to identify itemsets that have utility value greater than the threshold utility value. In this
paper we present a review of the various techniques and current scenario of research in mining high utility itemset also presented
advantages and limitations of various techniques for High Utility Itemset Mining. We mainly focus on the D2HUP and MAHUSP
approach and algorithms for high utility pattern mining with less memory utilization.
Shilpa Ghode : Asst. Prof. In Computer Technology Department
Kavikulguru Institute of Technology and Science, Ramtek
Data mining, Frequent Patterns, High Utility Pattern Mining, High Utility Itemsets, High Utility Mining Algorithms
A Utility mining is an apparent topic in data mining. The main
focus in the field of Utility Mining is not only Frequent Itemset
Mining but also the consideration of utility. Practically it has
been found that the utility is of great interest in industry if
considers with high utility itemsets. This research paper
presents a review of various existing high utility itemset mining
algorithms. The reviewed algorithms effectively mining high
utility itemsets based on the various data structure and
constraint techniques. This will be helpful for developing new
efficient and optimize techniques for high utility itemset mining.
However to discover patterns for large transactional datasets an
effective high utility pattern mining algorithm is required for
improving the performance and search space of high utility
itemsets. As the concept of High Utility Itemset Mining has a
vast opportunities to be researched, the future work will
incorporate soft computing methodologies for high utility
itmesets mining such as the intuitionistic fuzzy logic can be
explored in the field of High Utility Itemset Mining and its
memory consumption.
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