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 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

Data Stream Frequent Closed Item Sets Mining Based on Fast Sliding Window

2011 ◽  
Vol 130-134 ◽  
pp. 3702-3707
Author(s):  
Zhi Hua Chen ◽  
Jun Luo
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Screening Method ◽  
Hash Table ◽  
Sliding Window ◽  
Threshold Value ◽  
Space Efficiency ◽  
Frequent Item ◽  
Support Threshold ◽  
Frequent Item Sets

According to the mobility and continuity of the flow of data streams，this paper presents an algorithm called NSWR to mine the frequent item sets from a fast sliding window over data streams and it meets people’s needs of getting the frequent item sets over data that recently arrive. NWSR, using an effective bit-sequence representation of items based on the data stream sliding window, helps to store data; to support different support threshold value inquiry through hash-table-based frequent closed item sets results query method; to offer screening method based on the classification of closed item sets for reducing the number of item sets that need closure judgments, effectively reducing the computational complexity. Experiments show that the algorithm has better time and space efficiency.

Cross Sellingusing Association Rule Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1337-1341
Author(s):  
Ran Bo Yao ◽  
An Ping Song ◽  
Xue Hai Ding ◽  
Ming Bo Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
New Method ◽  
Experimental Result ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Direct Benefits ◽  
Frequent Item Sets

In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

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