An Efficient Algorithm for Distributed Incremental Updating of Frequent Item-Sets on Massive Database

2006 ◽  
pp. 61-72
Author(s):  
Jiangtao Qiu ◽  
Changjie Tang ◽  
Lei Duan ◽  
Chuan Li ◽  
Shaojie Qiao ◽  
...  
Keyword(s):  
Efficient Algorithm ◽  
Incremental Updating ◽  
Frequent Item ◽  
Frequent Item Sets ◽  
Massive Database

scholarly journals Mining Closed Item sets using Partition based Single Scan Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 3885-3889
Keyword(s):  
Efficient Algorithm ◽  
Empirical Evaluation ◽  
Frequent Itemsets ◽  
Frequent Item ◽  
Closed Itemsets ◽  
Frequent Item Sets

Closed item sets are frequent itemsets that uniquely determines the exact frequency of frequent item sets. Closed Item sets reduces the massive output to a smaller magnitude without redundancy. In this paper, we present PSS-MCI, an efficient candidate generate based approach for mining all closed itemsets. It enumerates closed item sets using hash tree, candidate generation, super-set and sub-set checking. It uses partitioned based strategy to avoid unnecessary computation for the itemsets which are not useful. Using an efficient algorithm, it determines all closed item sets from a single scan over the database. However, several unnecessary item sets are being hashed in the buckets. To overcome the limitations, heuristics are enclosed with algorithm PSS-MCI. Empirical evaluation and results show that the PSS-MCI outperforms all candidate generate and other approaches. Further, PSS-MCI explores all closed item sets.

scholarly journals An Efficient Algorithm for Mining Maximal Frequent Item Sets

2008 ◽  
Vol 4 (8) ◽  
pp. 638-645 ◽  
Author(s):  
A.M.J. Md. Zubair Rahman ◽  
P. Balasubram
Keyword(s):  
Efficient Algorithm ◽  
Frequent Item ◽  
Frequent Item Sets

scholarly journals An Efficient Algorithm for Mining Of frequent items using incremental model

2011 ◽  
pp. 18-22
Author(s):  
Nibedita Panigrahi ◽  
P.K. Pattnaik ◽  
S.K. Padhi
Keyword(s):  
Data Streams ◽  
Efficient Algorithm ◽  
Experimental Result ◽  
Current Frequency ◽  
The Past ◽  
Frequent Item ◽  
Incremental Model ◽  
Current State ◽  
Frequent Items ◽  
Frequent Item Sets

Data mining is a part of know ledge Discovery in database process (KDD). As technology advances, floods of data can be produced and shared in many appliances such as wireless Sensor networks or Web click streams. This calls for extracting useful information and knowledge from streams of data. In this paper, We have proposed an efficient algorithm, where, at any time the current frequencies of all frequent item sets can be immediately produced. The current frequency of an item set in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state. The experimental result shows the proposed algorithm not only maintains a small summery of information for one item set but also consumes less memory then existing algorithms for mining frequent item sets over recent data streams.

scholarly journals A Tree Projection Algorithm for Generation of Frequent Item Sets

2001 ◽  
Vol 61 (3) ◽  
pp. 350-371 ◽  
Author(s):  
Ramesh C. Agarwal ◽  
Charu C. Aggarwal ◽  
V.V.V. Prasad
Keyword(s):  
Projection Algorithm ◽  
Frequent Item ◽  
Frequent Item Sets

The Research of Improved Apriori Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2179-2184 ◽  
Author(s):  
Zhen Yun Liao ◽  
Xiu Fen Fu ◽  
Ya Guang Wang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Mining Algorithm ◽  
The Times ◽  
Frequent Item Sets ◽  
Candidate Item ◽  
Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

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