The Utility Frequent Pattern Mining Based on Slide Window in Data Stream

2012 ◽  
Author(s):  
Feng-gang Li ◽  
Ying-jia Sun ◽  
Zhi-wei Ni ◽  
Yu Liang ◽  
Xue-ming Mao
Keyword(s):  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern

An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

2012 ◽  
Vol 433-440 ◽  
pp. 4457-4462 ◽  
Author(s):  
Jun Shan Tan ◽  
Zhu Fang Kuang ◽  
Guo Gui Yang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Data Generation ◽  
Experiment Data ◽  
Mining Algorithm ◽  
Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

Mining Closed Weighed Frequent Patterns from a Sliding Window over Data Stream

2013 ◽  
Vol 756-759 ◽  
pp. 2606-2609
Author(s):  
Cui Cui Ge ◽  
Xiu Fen Fu
Keyword(s):  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Itemset ◽  
Experimental Results ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Itemset Mining ◽  
Basic Window

Weighted frequent pattern mining address to discover more important frequent pattern by considering different weights of every item, closed frequent pattern mining can significantly reduce the number of frequent itemset mining and keep sufficient result information. In this paper,we proposed an algorithm DS_CRWF to mine closed weighted frequent pattern over data stream,which is based on sliding window and take basic window as unit of updating,all the closed weighted frequent patterns can be mined through once scan.The experimental results show the feasibility of the algorithm.

A Novel Algorithm for Predicting Valuable Items in Data Streams

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
S. Vijayarani Mohan
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Research Work ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Items ◽  
And Performance ◽  
Transactional Data

A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous arrival of rapid data records. Data can arrive fast and in continuous manner. It is very difficult to perform mining process. Normally, stream mining algorithms are designed to scan the database only once, and it is a complicated task to extract the knowledge from the database by a single scan. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. Popular data mining techniques namely clustering, classification, and frequent pattern mining are applied to data streams for extracting the knowledge. This research work mainly concentrates on how to predict the valuable items which are found in a transactional data of a data stream. In the literature, most of the researchers have discussed about how the frequent items are mined from the data streams. This research work helps to predict the valuable items in a transactional data. Frequent item mining is defined as finding the items which occur frequently, i.e. the occurrence of items above the given threshold is considered as frequent items. Valuable item mining is nothing but finding the costliest or most valuable items of a database. Predicting this information helps businesses to know about the sales details about the valuable items which guide to make crucial decisions, such as catalogue drawing, cross promotion, end user shopping, and performance scrutiny. In this research work, a new algorithm namely VIM (Valuable Item Mining) is proposed for finding the valuable items in data streams. The performance of this algorithm is analysed by using the factors, number of valuable items discovered, and execution time.

scholarly journals Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jing Chen ◽  
Peng Li ◽  
Weiqing Fang ◽  
Ning Zhou ◽  
Yue Yin ◽  
...  
Keyword(s):  
Set Theory ◽  
Fuzzy Set Theory ◽  
Quantitative Data ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Medical Data ◽  
Frequent Pattern

Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP-T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates.

scholarly journals Framework for High Utility Pattern Mining using Dynamically Generated Minimum Support ThresholdFramework for High Utility Pattern Mining using Dynamically Generated Minimum Support Threshold

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1007
Author(s):  
Shankar B. Naik ◽  
Jyoti D. Pawar
Keyword(s):  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Pattern ◽  
Minimum Support ◽  
Itemset Mining ◽  
Support Threshold ◽  
High Utility

In this paper we have proposed a framework which uses high utility itemset mining to store data stream elements in a compressed form and then detect events from the sliding window. This approach promises to reduce the memory requirements when applied to frequent pattern mining in data streams.In addition to this, a method to dynamically define the value of minimum support threshold based on data in the data stream is presented.  

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