scholarly journals Knowledge Discovery from Web Usage Data: Research and Development of Web Access Pattern Tree Based Sequential Pattern Mining Techniques: A Survey

2010 ◽  
Author(s):  
G. Shivaprasad ◽  
N. V. Subbareddy ◽  
U. Dinesh Acharya ◽  
R. B. Patel ◽  
B. P. Singh
Keyword(s):  
Research And Development ◽  
Knowledge Discovery ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Access Pattern ◽  
Web Usage ◽  
Web Access ◽  
Web Access Pattern ◽  
Usage Data

scholarly journals Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment

2021 ◽  
Vol 13 (16) ◽  
pp. 8900
Author(s):  
Naeem Ahmed Mahoto ◽  
Asadullah Shaikh ◽  
Mana Saleh Al Reshan ◽  
Muhammad Ali Memon ◽  
Adel Sulaiman
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Analysis ◽  
Pattern Mining ◽  
Large Data ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Electronic Records ◽  
Data Mining Techniques ◽  
Healthcare Data

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.

Mining Closed Repetitive Gapped Sequential Patterns Based on Repetition Linked WAP-Tree

2012 ◽  
Vol 197 ◽  
pp. 283-291
Author(s):  
Jun Dong ◽  
Yu Jie Xie ◽  
Jia Dong Ren ◽  
Wei Wei Zhou
Keyword(s):  
Pattern Mining ◽  
Positional Information ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Position Information ◽  
Web Access ◽  
Frequent Items ◽  
Novel Method ◽  
Web Access Pattern ◽  
The Given

Closed repetitive gapped sequential pattern mining has been gained more and more attention in recent years, in this paper, we propose a novel method MRCGP(mining closed repetitive gapped sequential pattern based on repetition linked WAP-Tree). In the first step of MRCGP, the given sequential database is transformed into a new database in which every item is expressed by its landmark; then a positional information table(PIT) which includes all of the position information of 1-frequent items is constructed, all of the repetitive gapped 2-sequential patterns of different items (RPDI) can be obtained through searching the positional information table; following, a repetitive linked web access pattern tree (RLWAP-Tree) is built, in RLWAP-Tree, the 1-frequent items are stored as header table, the items in header table will be linked to their same items which appear earliest in each sequence corresponding to RLWAP-Tree with solid line, all of the items in RLWAP-Tree are linked to their same items in the same sequences with broken line; through mining projection tree of the existing repetitive gapped pattern recursively, we can obtain the repetitive gapped sequential pattern; at the end, we get the closed repetitive gapped sequential pattern by checking inclusion relation of any two patterns. The experiment result shows MRCGP has better time efficiency.

scholarly journals A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining

2021 ◽  
Vol 12 (2) ◽  
pp. 1634-1639
Author(s):  
V Aruna, Et. al.
Keyword(s):  
Web Mining ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Relevant Information ◽  
Sequential Pattern Mining ◽  
Web Usage Mining ◽  
Sequential Pattern ◽  
Web Usage ◽  
Hidden Data ◽  
The Web

In the recent years with the advancement in technology, a  lot of information is available in different formats and extracting the  knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem  Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one  of the subset of  Web Mining helps in extracting the hidden knowledge present in the Web log  files , in recognizing various interests of web users and also in  discovering customer behaviours. Web Usage mining  includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining  algorithms  like  apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy,  sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison  is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

Efficient Mining of Robust Closed Weighted Sequential Patterns Without Information Loss

2015 ◽  
Vol 24 (01) ◽  
pp. 1550007 ◽  
Author(s):  
Unil Yun ◽  
Gwangbum Pyun ◽  
Eunchul Yoon
Keyword(s):  
High Performance ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Information Loss ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Performance Tests ◽  
Web Access ◽  
Complete Set ◽  
Access Patterns

Sequential pattern mining has become one of the most important topics in data mining. It has broad applications such as analyzing customer purchase data, Web access patterns, network traffic data, DNA sequencing, and so on. Previous studies have concentrated on reducing redundant patterns among the sequential patterns, and on finding meaningful patterns from huge datasets. In sequential pattern mining, closed sequential pattern mining and weighted sequential pattern mining are the two main approaches to perform mining tasks. This is because closed sequential pattern mining finds representative sequential patterns which show exactly the same knowledge as the complete set of frequent sequential patterns, and weight-based sequential pattern mining discovers important sequential patterns by considering the importance of each sequential pattern. In this paper, we study the problem of mining robust closed weighted sequential patterns by integrating two paradigms from large sequence databases. We first show that the joining order between the weight constraints and the closure property in sequential pattern mining leads to different sets of results. From our analysis of joining orders, we suggest robust closed weighted sequential pattern mining without information loss, and present how to discover representative important sequential patterns without information loss. Through performance tests, we show that our approach gives high performance in terms of efficiency, effectiveness, memory usage, and scalability.

Sign in / Sign up

Export Citation Format

Share Document