scholarly journals Finding Frequent Itemsets based on Open Data Mining in Data Streams

2003 ◽  
Vol 10D (3) ◽  
pp. 447-458
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Open Data ◽  
Frequent Itemsets

scholarly journals A Sliding Window Technique for Open Data Mining over Data Streams

2005 ◽  
Vol 12D (3) ◽  
pp. 335-344
Author(s):  
Joong-Hyuk Chang ◽  
Won-Suk Lee
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Open Data ◽  
Sliding Window ◽  
Window Technique

A Meta-Mining Ontology Framework for Data Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 37-56
Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Streams ◽  
Specific Data ◽  
Ontology Merging ◽  
Knowledge Models ◽  
Task Requirements ◽  
Ontology Extraction ◽  
Merging Method ◽  
And Task

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

Using hashing and lexicographic order for Frequent Itemsets Mining on data streams

2019 ◽  
Vol 125 ◽  
pp. 58-71 ◽  
Author(s):  
Lázaro Bustio-Martínez ◽  
Martín Letras-Luna ◽  
René Cumplido ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe ◽  
...  
Keyword(s):  
Data Streams ◽  
Lexicographic Order ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining

scholarly journals Applying Logistic Regression Data mining techniques for Ethiopian Government Agricultural Open Data Sets

2021 ◽  
Vol 10 (2) ◽  
pp. 473-488
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Agricultural Sector ◽  
Model Building ◽  
Open Data ◽  
Research Process ◽  
Attribute Selection ◽  
Sufficient Evidence ◽  
Vegetable Crops ◽  
Logistic Regression Algorithm

Ethiopia has a great agricultural potential because of its vast areas of fertile land, diverse climate, generally adequate rainfall, and large labor force. With its verified importance to the Ethiopian economy, there is sufficient evidence to show that the potential of the agricultural sector can be expanded considerably by attracting investors towards the sector. This study aims at applying classification techniques in developing a predictive model that can estimate yield production of vegetable crops and the correlation of crops based on their class. In the process of building a model, different steps were undertaken. Among the steps, data collection, data preprocessing and model building and validation were the major ones. Different tasks performed in each step are mentioned as follows. The data were collected Food and Agriculture Organization of the United Nations (FAO). Under preprocessing, data cleaning, discretization and attribute selection were done. The final step was model building and validation and it was performed using the selected tools and techniques. The data mining tool used in this research was Weka. In this software the logistic regression algorithm was selected since it is capable to score more accuracy. After successive experiments were done using this software, a model that can classify crop yield as high, medium and low with better accuracy to the extent of 88.6%. Experimental results show that logistic regression is a very helpful tool to depict the contribution of yield estimation and crop correlation. The reported findings are optimistic, making the proposed model a useful tool in the decision making process. Eventually, the whole research process can be a good input for further indepth research

Approximate mining of global closed frequent itemsets over data streams

2011 ◽  
Vol 348 (6) ◽  
pp. 1052-1081 ◽  
Author(s):  
Lichao Guo ◽  
Hongye Su ◽  
Yu Qu
Keyword(s):  
Data Streams ◽  
Frequent Itemsets ◽  
Closed Frequent Itemsets

Exploring Calendar-Based Pattern Mining in Data Streams

2010 ◽  
pp. 342-360
Author(s):  
Rodrigo Salvador Monteiro ◽  
Geraldo Zimbrão ◽  
Holger Schwarz ◽  
Bernhard Mitschang ◽  
Jano Moreira de Souza
Keyword(s):  
Data Warehouse ◽  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
A Priori ◽  
Frequent Itemsets ◽  
Detailed Data ◽  
Series Of Experiments ◽  
Working Day

Calendar-based pattern mining aims at identifying patterns on specific calendar partitions. Potential calendar partitions are for example: every Monday, every first working day of each month, every holiday. Providing flexible mining capabilities for calendar-based partitions is especially challenging in a data stream scenario. The calendar partitions of interest are not known a priori and at each point in time only a subset of the detailed data is available. The authors show how a data warehouse approach can be applied to this problem. The data warehouse that keeps track of frequent itemsets holding on different partitions of the original stream has low storage requirements. Nevertheless, it allows to derive sets of patterns that are complete and precise. Furthermore, the authors demonstrate the effectiveness of their approach by a series of experiments.

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