Knowledge extraction from database is the fundamental task in and data mining community, which has been applied to a wide range of real-world applications situations. Different support-based models, utility-oriented framework integrates utility theory provide more informative useful patterns. Time-dependent sequence are commonly seen real life. Sequence have widely utilized many applications, s...

Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability interpretability. To obtain a model for decision making, this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. Meanwhile, more normative formulation of the problem fuzzy sequences is formulated. By explorin...

Conventional Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. Until recently, rarity has not received much attention in the context of data mining. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. Most Association Rule...

Utility mining has recently been an emerging topic in the field of data mining. It finds out high utility itemsets by considering both the profits and quantities of items in transactions. It may have a bias if items are not always on shelf. In this paper, we thus design a new kind of patterns, named high on-shelf utility itemsets, which considers not only individual profit and quantity of each ...

Service Oriented Computing which use Knowledge as a service makes the use of Utility Mining approach. Here, we have proposed an architecture called Knowledge as a Service (KaaS) where we use utility mining algorithms for extracting the knowledge data from the data owners when the knowledge consumers are in need of a particular knowledge data. The main motive behind proposing architecture is to ...

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPS...

Data mining can be used extensively in the enterprise based applications with business intelligence characteristics to provide a deeper kind of analysis while meeting strict requirements for administration management and security. Business intelligence is information about a company's past performance that is used to help predict the company's future performance. ARM is a well-known technique i...

Mobile sequential pattern mining is an emerging topic in data mining fields with wide applications, such as planning mobile commerce environments and managing online shopping websites. However, an important factor, i.e., actual utilities (i.e., profit here) of items, is not considered and thus some valuable patterns cannot be found. Therefore, previous researches [8, 9] addressed the problem of...

High utility sequential pattern (HUSP) mining has emerged as a novel topic in data mining. Although some preliminary works have been conducted on this topic, they incur the problem of producing a large search space for high utility sequential patterns. In addition, they mainly focus on mining HUSPs in static databases and do not take streaming data into account, where unbounded data come contin...

High utility sequential pattern mining has emerged as an important topic in data mining. Although several preliminary works have been conducted on this topic, the existing studies mainly focus on mining high utility sequential patterns (HUSPs) in static databases and do not consider the streaming data. Mining HUSPs over data streams is very desirable for many applications. However, addressing t...