The Algorithm for Data Mining Frequent Patterns over Sliding Window

2014 ◽  
Vol 513-517 ◽  
pp. 759-762
Author(s):  
Xiao Lei Zhao ◽  
Wei Huang
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Sliding Window ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Sliding Windows ◽  
Space Efficiency ◽  
Tree Pattern ◽  
Data Generator

On the basis of the shortcoming of the existed algorithm, this paper probes into sliding windows pattern and introduces an efficient algorithm for data mining frequent pattern over sliding windows. A PSW-tree pattern is set in the algorithm to store frequent and critical pattern in data mining. On this basis, the paper presents a rapid mining algorithmPSW algorithm. In the experiment IBM data generator is used to produce generated data, which proves the validity and better space efficiency of the algorithm.

scholarly journals BIG DATA MINING FOR INTERESTING PATTERNS WITH MAP REDUCE TECHNIQUE

2017 ◽  
Vol 10 (13) ◽  
pp. 191
Author(s):  
Nikhil Jamdar ◽  
A Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Uncertain Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Map Reduce ◽  
Frequent Patterns ◽  
Precise Data ◽  
Big Data Mining ◽  
Transactional Databases

There are many algorithms available in data mining to search interesting patterns from transactional databases of precise data. Frequent pattern mining is a technique to find the frequently occurred items in data mining. Most of the techniques used to find all the interesting patterns from a collection of precise data, where items occurred in each transaction are certainly known to the system. As well as in many real-time applications, users are interested in a tiny portion of large frequent patterns. So the proposed user constrained mining approach, will help to find frequent patterns in which user is interested. This approach will efficiently find user interested frequent patterns by applying user constraints on the collections of uncertain data. The user can specify their own interest in the form of constraints and uses the Map Reduce model to find uncertain frequent pattern that satisfy the user-specified constraints 

scholarly journals Preference-Based Frequent Pattern Mining

2008 ◽  
pp. 1280-1299
Author(s):  
Moonjung Cho ◽  
Jian Pei ◽  
Haixun Wang ◽  
Wei Wang
Keyword(s):  
Data Mining ◽  
General Framework ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Performance Study ◽  
Important Data ◽  
Mining Algorithms ◽  
Extensive Performance

Frequent pattern mining is an important data-mining problem with broad applications. Although there are many in-depth studies on efficient frequent pattern mining algorithms and constraint pushing techniques, the effectiveness of frequent pattern mining remains a serious concern: It is non-trivial and often tricky to specify appropriate support thresholds and proper constraints. In this paper, we propose a novel theme of preference-based frequent pattern mining. A user simply can specify a preference instead of setting detailed parameters in constraints. We identify the problem of preference-based frequent pattern mining and formulate the preferences for mining. We develop an efficient framework to mine frequent patterns with preferences. Interestingly, many preferences can be pushed deep into the mining by properly employing the existing efficient frequent pattern mining techniques. We conduct an extensive performance study to examine our method. The results indicate that preference-based frequent pattern mining is effective and efficient. Furthermore, we extend our discussion from pattern-based frequent pattern mining to preference-based data mining in principle and draw a general framework.

scholarly journals An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams

2009 ◽  
Vol E92-D (7) ◽  
pp. 1369-1381 ◽  
Author(s):  
Chowdhury Farhan AHMED ◽  
Syed Khairuzzaman TANBEER ◽  
Byeong-Soo JEONG ◽  
Young-Koo LEE
Keyword(s):  
Data Streams ◽  
Efficient Algorithm ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Pattern

scholarly journals Frequent Pattern Mining over Unstructured Data using Semi-Structured Doc-Model and Pattern Ranking

2020 ◽  
pp. 36-42
Author(s):  
Sudhir Tirumalasetty ◽  
A. Divya ◽  
D. Rahitya Lakshmi ◽  
Ch. Durga Bhavani ◽  
D. Anusha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Unstructured Data ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Innovative Methods ◽  
Mining Algorithms ◽  
Doc Model

Frequent pattern mining is an essential data-mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern-mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. This paper reviews recent advances in parallel frequent pattern mining, analysing them through the Big Data lens. Load balancing and work partitioning are the major challenges to be conquered. These challenges always invoke innovative methods to do, as Big Data evolves with no limits. The biggest challenge than before is conquering unstructured data for finding frequent patterns. To accomplish this Semi Structured Doc-Model and ranking of patterns are used.

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.

scholarly journals An improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules

2019 ◽  
Vol 9 (2S2) ◽  
pp. 776-780
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Tree Construction ◽  
Generalized Association Rules ◽  
Education Marketing ◽  
The Given

This research focuses on mining the frequent patterns occurred in the given Datasets by Generalization of Association Rules. Frequent pattern mining is a significant as well as interesting problem in the research filed of Data Mining. Building of frequent pattern tree (FP tree), frequent pattern growth (FP growth) and association rule generation are implemented to find the repeated patterns of data. FP tree Construction Algorithm is mainly used to compact a vast database into a extremely compressed, seems to very tiny data structure; hence eliminates the repeated scanning of database. The role of FP growth algorithm is to discover the frequent patterns with FP tree structure and construct the generalized association rules corresponding to the learning data. FP growth algorithm obtained best results as compared with conventional Apriori algorithm. This research provides some practical real time applications pertaining data mining techniques in the field of learning, education, marketing, finance and so on.

scholarly journals The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

2010 ◽  
Vol 10 (3) ◽  
pp. 251-289 ◽  
Author(s):  
JOANNA JÓZEFOWSKA ◽  
AGNIESZKA ŁAWRYNOWICZ ◽  
TOMASZ ŁUKASZEWSKI
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Knowledge Base ◽  
Description Logics ◽  
Pattern Discovery ◽  
Knowledge Bases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Representation Formalism

AbstractWe propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

scholarly journals FP-outlier: Frequent pattern based outlier detection

2005 ◽  
Vol 2 (1) ◽  
pp. 103-118 ◽  
Author(s):  
Zengyou He ◽  
Xiaofei Xu ◽  
Zhexue Huang ◽  
Shengchun Deng
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Frequent Itemsets ◽  
Research Community ◽  
Experimental Results ◽  
New Method ◽  
Frequent Pattern ◽  
Data Detection ◽  
Frequent Patterns ◽  
Data Set

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.

Mining Maximal Frequent Patterns over Data Stream Based on Time Decaying

2014 ◽  
Vol 602-605 ◽  
pp. 3835-3838
Author(s):  
Fen Fen Zhou ◽  
Jun Rui Yang
Keyword(s):  
Data Stream ◽  
Search Strategy ◽  
Sliding Window ◽  
Search Space ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Depth First Search ◽  
Maximal Frequent Pattern ◽  
Frequent Pattern Tree ◽  
Enumeration Tree

A new algorithm DSMFP-Miner was proposed. When the data stream reach continuously, a maximal frequent pattern tree called MFP-Tree is adopted to maintain the transactions in data screams dynamically. Transactions in the same Transaction-sensitive sliding window are set to own the same “importance”. Besides, the support of the transactions in old window is decayed to reduce their influence to mining results, and infrequent patterns and overdue patterns are deleted periodically. In the mining process, the algorithm put an enumeration tree with each node of MFP-Tree as root as the search space, and use the "depth-first" search strategy to mining the maximal frequent patterns with this node as a suffix.

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