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A High-Performance Algorithm for Identifying Frequent Items in Data Streams [article]

Daniel Anderson, Pryce Bevan, Kevin Lang, Edo Liberty, Lee Rhodes, Justin Thaler
2017 arXiv   pre-print
Estimating frequencies of items over data streams is a common building block in streaming data measurement and analysis.  ...  We describe a highly optimized version of Misra and Gries' algorithm that is suitable for deployment in industrial settings.  ...  We are grateful to Graham Cormode for helpful comments on an earlier version of this manuscript.  ... 
arXiv:1705.07001v2 fatcat:ntjupbz7qzdqhalimamnnjmv34

Frequent items in streaming data: An experimental evaluation of the state-of-the-art

Nishad Manerikar, Themis Palpanas
2009 Data & Knowledge Engineering  
In summary, in this work we make the following contributions. • We evaluate the performance of the most prominent algorithms proposed in the literature for the problem of identifying frequent items in  ...  Consequently, the area of data stream mining has received considerable attention in the recent years. An important problem in data stream mining is that of finding frequent items in the stream.  ...  Frequent items are identified by performing 'majority tests', i.e., by identifying items which occur more than half the time in a group.  ... 
doi:10.1016/j.datak.2008.11.001 fatcat:gm7ux5tmzfe7vo35ter33fneoy

Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data

2019 International Journal of Engineering and Advanced Technology  
The main objective of the paper is to analyze the algorithm and performance metrics related to the frequent patter mining or relevant data.  ...  Various frequent pattern mining algorithm is analyzed and review has been carried out based on the performance level.  ...  Randomized algorithm is also applied for selecting random samples for generating frequent items with high confidence level.  ... 
doi:10.35940/ijeat.f9352.088619 fatcat:7mfk3kmldzhclkt5a67astv7tm

Method presented for finding Frequent Itemsets in web data streams

Farzaneh Kaviani, Mohammad Reza Khayyambashi
2016 Journal of Soft Computing and Applications  
In this article, a new vertical display and an algorithm is provided based on the pins in order to find frequent itemsets in data streams.  ...  On the other hand, according to properties of data stream which are unlimited productions with a high-speed, it is not possible saving these data on memory and we need for techniques which enables online  ...  A topic which has received much attention is how to find frequent items in data streams.  ... 
doi:10.5899/2016/jsca-00065 fatcat:ca32uemcire65jurbdz6pu2aji

Proposing a Algorithm for Finding Repetitive Patterns in Web Dataflow

Mohammad Rostami, Somayyeh Ehteshami, Fatemeh Yaghoobi, Farid Saghari, Samaneh Dezhdar
2015 International Journal of Software Engineering and Its Applications  
Since this new display without any additional task has a compact form, the proposed algorithm has a better performance than similar ones in terms of consumed memory and processing time.  ...  In this paper, a new vertical display and an algorithm based on pins, called DBP-BA, are proposed to find repetitive patters in data flows.  ...  They are unlimited chains of data being created continuously at a very high speed. If an item set's number of occurrence exceeds a specific limit it is identified to be frequent.  ... 
doi:10.14257/ijseia.2015.9.7.19 fatcat:h7c2s3zcpndvxjcyokpz5ctyb4

Overview of Itemset Utility Mining and its Applications

Jyothi Pillai, O.P. Vyas
2010 International Journal of Computer Applications  
In this paper, a literature survey of various algorithms for high utility rare itemset mining has been presented.  ...  Itemset Utility Mining is an extension of Frequent Itemset mining, which discovers itemsets that occur frequently. In many real-life applications, high-utility itemsets consist of rare items.  ...  An algorithm for frequent item set mining was presented by J. Hu et al in [13] that identify highutility item combinations.  ... 
doi:10.5120/956-1333 fatcat:dro3py2glzaftkmxpfpn5rvv4e

Frequent Pattern Retrieval on Data Streams by using Sliding Window

P. Kumar, P. Rao
2021 EAI Endorsed Transactions on Energy Web  
In this paper, the Frequent Pattern Retrieval strategy is planned by utilizing an FP tree approach and a sliding window model to extract noteworthy examples from data streams.  ...  In different applications like recommender frameworks and market examination, regular patterns play a significant role in useful mining data.  ...  Therefore, the process of identifying the patterns in those data streams leads to a high-challenging task due to the presence of a vast amount of data that are scanned often with high memory consumption  ... 
doi:10.4108/eai.13-1-2021.168091 fatcat:ud3pbbgmq5gl3b525spburyl7i

Mining Frequent Itemsets (MFI) Over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions

Sidda Reddy V, Rao T.V, Govardhan A
2014 International Journal of Data Mining & Knowledge Management Process  
The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper.  ...  There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this design we have used window size change to represent the conceptual drift in an information stream.  ...  Experimental results We compare our algorithm with frequent itemsets mining model for data streams devised in [13] , which is a matrix based frequent itemsets mining (MFIM) algorithm for data streams.  ... 
doi:10.5121/ijdkp.2014.4402 fatcat:cwsnperklfdzdfxegyp26kigd4

Mining Frequent Itemsets (MFI) over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions [article]

V.Sidda Reddy, Dr.T.V.Rao, Dr.A.Govardhan
2014 arXiv   pre-print
The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper.  ...  There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this design we have used window size change to represent the conceptual drift in an information stream.  ...  Experimental results We compare our algorithm with frequent itemsets mining model for data streams devised in [13] , which is a matrix based frequent itemsets mining (MFIM) algorithm for data streams.  ... 
arXiv:1408.3175v1 fatcat:ih7orjk6tbaixpn7rqik5xebvq

A New Data Stream Mining Algorithm for Interestingness-Rich Association Rules

Venu Madhav Kuthadi
2013 Journal of Computer Information Systems  
Frequent itemset mining and association rule generation is a challenging task in data stream.  ...  However, fewer algorithms exist in the literature to deal with the utility as most of them deals with reducing the complexity in frequent itemset/association rules mining algorithm.  ...  This motivates our research in developing a new efficient data stream mining algorithm for mining frequent rules from the data stream.  ... 
doi:10.1080/08874417.2013.11645628 fatcat:g7nm3wbmsfd77ckdwspdkzuag4

A Survey on Mining Frequent Itemsets over Data Streams

Shailvi Maurya, Sneha Ambhore, Sneha Parit
2017 International Journal of Computer Applications  
Thus the paper provides different algorithms for mining over static and dynamic data also known as data stream.  ...  Mining frequent itemsets over data stream has been challenging task.  ...  speed should be high for better performance because slow computing will lead to high time consumption.  Data Scan: The scanning of data must be done once due to the dynamic nature of the data stream  ... 
doi:10.5120/ijca2017916030 fatcat:ojkfobsynnedhhp7d7ayxabo7a

DSM-FI: an efficient algorithm for mining frequent itemsets in data streams

Hua-Fu Li, Man-Kwan Shan, Suh-Yin Lee
2008 Knowledge and Information Systems  
In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions  ...  SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far.  ...  The proposed DSM-FI algorithm In this section, we describe the proposed algorithm DSM-FI (data stream mining for frequent itemsets) for online mining of frequent itemsets in a landmark window of a continuous  ... 
doi:10.1007/s10115-007-0112-4 fatcat:v4ildu6wkbfp3eqdhw2h54ke74

A Sketch-Based Architecture for Mining Frequent Items and Itemsets from Distributed Data Streams

Eugenio Cesario, Antonio Grillo, Carlo Mastroianni, Domenico Talia
2011 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing  
In particular, data stream analysis has been carried out for the computation of items and itemsets that exceed a frequency threshold.  ...  The mining approach is hybrid, that is, frequent items are calculated with a single pass, using a sketch algorithm, while frequent itemsets are calculated by a further multi-pass analysis.  ...  The algorithm for the computation of frequent items, outlined in Figure 3 , is performed continuously, for every new block of data that is generated by the data streams.  ... 
doi:10.1109/ccgrid.2011.45 dblp:conf/ccgrid/CesarioGMT11 fatcat:dk2w2qlquvc7lmzrqdru6vxkmq

An Extensive Review of Significant Researches in Data Mining

Paul P. Mathai, R.V. Siva Balan
2014 Research Journal of Applied Sciences Engineering and Technology  
Moreover we present a concise description about the Data Mining techniques, performance review and the instructions for future research.  ...  In this study, we provide a comprehensive survey and study of various methods in existence for item set mining based on the utility and frequency and association rule mining based research works and also  ...  A novel tree structure, called HUS-tree (High Utility Stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data  ... 
doi:10.19026/rjaset.7.865 fatcat:nmzomgruzfgvlkzy3vtxe4ugre

Finding the frequent items in streams of data

Graham Cormode, Marios Hadjieleftheriou
2009 Communications of the ACM  
In this paper, we describe the most important algorithms for this problem in a common framework.  ...  The frequent items problem is to process a stream of items and find all those which occur more than a given fraction of the time.  ...  This research is ongoing, cementing the position of the frequent items problem as one of the most enduring and intriguing in the realm of algorithms for data streams.  ... 
doi:10.1145/1562764.1562789 fatcat:ufomhygco5hzdba26acbynm2bq
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