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Knowledge Fusion Technique Using Classifier Ensemble by Different Classification Rules

J. B. Patil, Prof. V.S. Nandedkar
2015 International Journal of Innovative Research in Computer and Communication Engineering  
In data mining applications knowledge extraction is splitted into subtasks due to memory or run-time limitations.  ...  Here its focused around the utilization of probabilistic generative classifiers utilizing multinomial circulations and multivariate ordinary dispersions for the consistent ones.  ...  And its also a central unit would constitute a single point of failure. Knowledge extraction is split into subtasks due to runtime limitations in other data mining applications.  ... 
doi:10.15680/ijircce.2015.0308022 fatcat:phdqdcqtgvbtzfl3ceomoly4ei

A study on sequential pattern mining on chemical information

S Sathya, N Rajendran
2018 International Journal of Engineering & Technology  
However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications.  ...  Data mining (DM) is used for extracting the useful and non-trivial information from the large amount of data to collect in many and diverse fields.  ...  Fusing classifiers was planned in [7] to remove the sample data from classification. It is derived from the applications of probabilistic generative classifiers (CMM) by multinomial distributions.  ... 
doi:10.14419/ijet.v7i2.33.14828 fatcat:4xofoypxcnhodolqoywc256mqe

A Decision Support System for Predicting Student Performance

Lalit Dole, Jayant Rajurkar
2014 International Journal of Innovative Research in Computer and Communication Engineering  
In recent years data mining has been successfully implemented in the business world.  ...  Predicting educational outcome is a practical alternative heterogeneous environment. Performance prediction models can be built by applying data mining techniques to enrolment data.  ...  This classifier represents the promising approach to the probabilistic discovery of knowledge, and it provides a very efficient algorithm for data classification. V.  ... 
doi:10.15680/ijircce.2014.0212015 fatcat:nfi52deuqbdrxjbupqn64m4ubu

A Survey on Efficient Clustering Methods with Effective Pruning Techniques for Probabilistic Graphs

M. Balaganesh, G.Bharathikannan G.Bharathikannan
2015 International Journal of Computer Applications  
This paper provides a survey on K-NN queries, DCR query, agglomerative complete linkage clustering and Extension of edit-distance-based definition graph algorithm and solving decision problems under uncertainty  ...  Finally, The Graph algorithm to understand how to mining can be done efficiently. This survey introduced to design algorithm for searching and to evaluate the algorithm throw analysis.  ...  The data mining be able to help in predicting a trend or value, classifying, categorizing the data, and in finding correlations, patterns from the data set.  ... 
doi:10.5120/19979-0721 fatcat:htcebdqalba7xliy475o3hvuyi

Text Mining Using Metadata for Generation of Side Information

Shraddha S. Bhanuse, Shailesh D. Kamble, Sandeep M. Kakde
2016 Procedia Computer Science  
In future, there is a scope to design an extended approach for clustering using classical partitioning and probabilistic model.  ...  In many metadata based text mining applications, side information also known as metadata which is associated with the text document.  ...  Naive Bays classifier 31 is a simple probabilistic classifier based Byes' theorem with strong (naive) assumptions.  ... 
doi:10.1016/j.procs.2016.02.061 fatcat:7rjgaacqkneetdbmfktpi2o6wa

An Improved Heart Disease Classification System Using Probabilistic Principal Component Analysis and K-Nearest Neighborhood

Akinyemi Omololu Akinrotimi, Dayo Reuben Aremu
2018 Advances in Multidisciplinary & Scientific Research Journal Publication  
In this paper, the Probabilistic Principal Component Analysis (PPCA), is used to preprocess and extract components from a clinical dataset, in order to provide a more organized and detailed data to be  ...  While Data mining techniques have proved to be effective in building models illustrating important data classes, especially where class attribute is involved in the construction of the classifier, K-Nearest  ...  K-Nearest-Neighbor is one of the most widely used data mining techniques in classification problems. Its simplicity and relatively high convergence speed make it a popular choice.  ... 
doi:10.22624/aims/v4n3p6 fatcat:uu47tgbpx5bqja2hxamohgknve

Market Basket Analysis with Enhanced Support Vector Machine (ESVM) Classifier for Key Security in Organization

2019 International Journal of Engineering and Advanced Technology  
The classifier makes use of information about several often utilized itemsets and it provides a key value to the actual company.  ...  Market Basket Analysis is considered to be one among the highly popular and efficient sort of data analysis exploited in the marketing and retailing field.  ...  Proposed system It starts with the mining of the frequent item set out of the database employing association rule mining for providing this information to the company. A.  ... 
doi:10.35940/ijeat.b3186.129219 fatcat:xh2bbwryhrajhgggtjsgzrd2ue

A Systematic study of Text Mining Techniques

Pravin Shinde, Sharvari Govilkar
2015 International Journal on Natural Language Computing  
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information  ...  Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations.  ...  So, it is not surprising to find that data mining and text mining systems have many high-level architectural similarities.  ... 
doi:10.5121/ijnlc.2015.4405 fatcat:pv6lawef7ngvzc7xwnf5aaj4ou

A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques [article]

Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut
2017 arXiv   pre-print
Additionally, we briefly explain text mining in biomedical and health care domains.  ...  In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering.  ...  Similarly, It has vast application in domains such as biomedical text mining and business intelligence. See [1] for some of the applications of information extraction.  ... 
arXiv:1707.02919v2 fatcat:uiwsrz6wgrb65dcnp54wmkrase

Applicability of Data Mining Technique Using Bayesians Network in Diagnosis of Genetic Diseases

Hugo Pereira
2013 International Journal of Advanced Computer Science and Applications  
So, it has been used classification techniques based in decision trees, probabilistic networks (Naïve Bayes, TAN e BAN) and neural MLP network (Multi-Layer Perception) and training algorithm by error retro-propagation  ...  Described tools capable of propagating evidence and developing techniques of generating efficient inference techniques to combine expert knowledge with data defined in a database.  ...  STATE OF THE ART TECHNIQUE IN AI AND DATA MINING The primary objectives of the research are to develop efficient inference techniques for use in information system, for which it is necessary the availability  ... 
doi:10.14569/ijacsa.2013.040107 fatcat:4ftmqtydyfe5hfzrziog3yd5uu


Surya Bhupal Rao
We also explain briefly text mining in the fields of biomedicine and health care.  ...  In this paper, we discuss several of the most basic tasks and techniques of text mining, including pre-processing, classification, and clustering.  ...  Although its architecture is simple and originally developed for indexing and data collection, VSMs are commonly used in specific text mining algorithms and IR systems and enable efficient analysis of  ... 
doi:10.26782/jmcms.spl.5/2020.01.00010 fatcat:r7wxb3eyrrd75bownfhzosqzci

A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review

Parvathy G., Bindhu J.S.
2016 International Journal of Computer Applications  
It allows the users to analyze the data, categorize them and identifies the relationship inferred in them.  ...  An application of this is to scan a set of documents in natural language for predictive classification purposes.  ...  Generative models have a wide variety of applications in text mining, language processing and information retrieval.  ... 
doi:10.5120/ijca2016908121 fatcat:53b4jordtnbothilf5eghnig4q

Similarity search and mining in uncertain databases

Matthias Renz, Reynold Cheng, Hans-Peter Kriegel
2010 Proceedings of the VLDB Endowment  
In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern  ...  There is a number of challenges in terms of collecting, modelling, representing, querying, indexing and mining uncertain data.  ...  This tutorial will give a survey of and classify the various approaches for probabilistic similarity queries, indexing uncertain data and data mining on uncertain objects proposed for the different uncertainty  ... 
doi:10.14778/1920841.1921066 fatcat:rqjstjk4h5fe3kzy7b462yvn3e

Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer

Shweta Kharya, Shika Agrawal, Sunita Soni
2014 International Journal of Computer Applications  
, a Probabilistic Classifier.  ...  The aim of this work is to design a Graphical User Interface to enter the patient screening record and detect the probability of having Breast cancer disease in women in her future using Naive Bayes Classifiers  ...  The data used in their investigation is the breast cancer data. It has a total of 6291 data and a dimension of 699 rows and 9 columns.  ... 
doi:10.5120/16045-5206 fatcat:pb3dgdieoneczj7azxriomdpsa

Probabilistic Measures for Interestingness of Deviations - A Survey

Adnan Masood, Sofiane Ouaguenouni
2013 International Journal of Artificial Intelligence & Applications  
However, study of literature suggests that interestingness is difficult to define quantitatively and can be best summarized as, a record or pattern is interesting if it suggests a change in an established  ...  In this age of data deluge with modern computing capabilities, we gather, distribute, and store information in vast amounts from diverse data sources.  ...  It distinguishes itself from McGarry's work in departing from a data mining context and instead focusing on measure categorization and behavior.  ... 
doi:10.5121/ijaia.2013.4201 fatcat:3bwuzx3egbc7xk62g3zosizrcu
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