MFTPM: Maximum Frequent Traversal Pattern Mining with Bidirectional Constraints
نویسندگان
چکیده
منابع مشابه
MFTPM: Maximum Frequent Traversal Pattern Mining with Bidirectional Constraints
An important application of sequential mining technique is maximal frequent traversal pattern mining, since users’ traversal pattern and motivation are latent in session sequence at some time segment. In this paper, a Frequent Traversal Pattern Tree structure with dwell time (FTP-Tree) is designed to store, compress the session database, and simplify the configuration of dwell time thresholds d...
متن کاملPushing Tougher Constraints in Frequent Pattern Mining
In this paper we extend the state-of-art of the constraints that can be pushed in a frequent pattern computation. We introduce a new class of tough constraints, namely Loose Anti-monotone constraints, and we deeply characterize them by showing that they are a superclass of convertible anti-monotone constraints (e.g. constraints on average or median) and that they model tougher constraints (e.g....
متن کاملApproximate Frequent Pattern Mining
Frequent pattern mining has been a focused theme in data mining research and an important first step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent pattern requires that every item occurs in each supporting transaction. However, real application data is usually subject to random noise or measurement error, which poses new challenges fo...
متن کاملEfficient Frequent Pattern Mining
Proefschrift voorgelegd tot het behalen van de graad van Doctor in de Wetenschappen, richting Informatica te verdedigen door Acknowledgements Many people have contributed to the realization of this thesis. First an foremost, I am grateful to my advisor Jan Van den Bussche for his guidance throughout my doctoral studies and all the time and effort he put in the development of me and my work. The...
متن کاملMining Frequent Partite Episodes with Partwise Constraints
In this paper, we study the problem of efficiently mining frequent partite episodes that satisfy partwise constraints from an input event sequence. Through our constraints, we can extract episodes related to events and their precedent-subsequent relations, on which we focus, in a short time. This improves the efficiency of data mining using trial and error processes. A partite episode of length...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computer Science
سال: 2006
ISSN: 1549-3636
DOI: 10.3844/jcssp.2006.704.709