Mining Prominent Closed Frequent Item sets from Data Streams using Dynamic and Adaptive Minimum Support Threshold

2018 ◽  
Author(s):  
Pavitra Bai S. ◽  
Ravi Kumar G.K.
Keyword(s):  
Data Streams ◽  
Minimum Support ◽  
Frequent Item ◽  
Support Threshold ◽  
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.

AFARTICA

2019 ◽  
Vol 30 (3) ◽  
pp. 71-93
Author(s):  
Saubhik Paladhi ◽  
Sankhadeep Chatterjee ◽  
Takaaki Goto ◽  
Soumya Sen
Keyword(s):  
Threshold Value ◽  
Search Space ◽  
Apriori Algorithm ◽  
The Novel ◽  
Novel Technique ◽  
Frequent Item ◽  
Artificial Cell ◽  
Typical Item ◽  
Support Threshold ◽  
Frequent Item Sets

Frequent item-set mining has been exhaustively studied in the last decade. Several successful approaches have been made to identify the maximal frequent item-sets from a set of typical item-sets. The present work has introduced a novel pruning mechanism which has proved itself to be significant time efficient. The novel technique is based on the Artificial Cell Division (ACD) algorithm which has been found to be highly successful in solving tasks that involve a multi-way search of the search space. The necessity conditions of the ACD process have been modified accordingly to tackle the pruning procedure. The proposed algorithm has been compared with the apriori algorithm implemented in WEKA. Accurate experimental evaluation has been conducted and the experimental results have proved the superiority of AFARTICA over apriori algorithm. The results have also indicated that the proposed algorithm can lead to better performance when the support threshold value is more for the same set of item-sets.

scholarly journals Partition based Single Scan Method for Mining Frequent Item Sets

2019 ◽  
Vol 8 (6) ◽  
pp. 4917-4922
Keyword(s):  
Unique Feature ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Minimum Support ◽  
Itemset Mining ◽  
Highly Sensitive ◽  
Support Threshold ◽  
Hidden Patterns ◽  
The Cost ◽  
Frequent Item Sets

Frequent Itemset mining (FIM) concept and limitations are explored in this paper, for the purpose of extracting unknown hidden patterns as itemsets from the transactional database. Since candidate generation and support calculations are the major tasks in FIM, the major limitations of FIM are tackled, (i) huge possible frequent itemsets are generated as candidates at each pass (ii) Data base scan at each pass to calculate the support of the generated itemsets (iii) generated itemsets are highly sensitive to the minimum support threshold. SS-FIM a single scan algorithm is to deal with the above limitations. However, several unnecessary itemsets are being hashed in the buckets. To overcome the limitations, a partition based approach is proposed in this paper. The proposed approach, PSSFIM, takes single scan of the database to identify frequent itemsets. The unique feature of PSSFIM allow to generate size of candidate itemsets independent on the minimum support. It allows the candidates in hash that are possible for frequent, which intuitively reduces the cost in terms of verifying the support of generated candidates. It is compared with SS-FIM and Apriori with the standard datasets. The results show that the PSSFIM is good at the comparison of SS-FIM and Apriori.

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 study of frequent itemset mining techniques

2017 ◽  
Vol 6 (4) ◽  
pp. 141
Author(s):  
Sachin Sharma ◽  
Shaveta Bhatia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Minimum Threshold ◽  
Minimum Support ◽  
Itemset Mining ◽  
Frequent Item ◽  
Frequent Item Sets ◽  
Different Sources

Frequent item set is the most crucial and expensive task for the industry today. It is the task of mining the information from different sources and a key approach in Data Mining. Frequent item sets satisfying the minimum threshold can be discovered. Association rules are extracted from frequent item sets. The Association rules are affected by the minimum support value entered by the user may be considered as Positive or negative. There may be some other Association rules, which involve the rare item sets. Various methods have been used by researchers for generating the Association Rules. In this paper, our aim is to study various techniques to generate the Association rules.

An Efficient Closed Frequent Item Sets Mining Algorithm-For Mining Closed Frequent Item Sets from Data Streams

2016 ◽  
Vol 13 (10) ◽  
pp. 7467-7474
Author(s):  
Venu Madhav Kuthadi ◽  
Rajalakshmi Selvaraj
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Processing Time ◽  
Frequent Itemset ◽  
Memory Usage ◽  
Data Set ◽  
Frequent Item ◽  
Mining Algorithm ◽  
Data Elements ◽  
Frequent Item Sets

A data stream is a continuous sequence of data elements generated from a specified source. Mining frequent item sets in dynamic databases and data streams encounters some challenges that make the mining task harder than static databases. Many research works were developed in the frequent itemset mining, but these methods have the familiar problem of memory usage and processing time. Because, in data streams data elements are arrive at a rapid rate. The incoming data is unbounded and probably infinite. Due to high speed and large amount of incoming data, frequent item set mining algorithm must require a limited memory and processing time. To reduce this drawback in the existing method, a new algorithm is proposed in this paper. Here, a new algorithm is named as CFIM is developed for mining closed frequent item sets from the data streams based on their utility and consistency. During the closed frequent item sets mining, a hash table is maintained to check whether the given item set is closed or not. The computation of closed frequent item sets from the data stream will minimize the memory usage and processing time. Thus our proposed technique performance is analyzed by using the synthetic data set and compared with the exiting mining techniques.

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