Recent Research Ideas in Data Mining

Recent Research Topics in Data Mining which is a significant process and are broadly utilized among organizations for addressing the business-related issues are shared in this page. Encompassing the novel areas, latest trends and existing problem, we provide several interesting as well as research-worthy topics along with potential solutions in the area of data mining:

Data Mining for COVID-19 Trend Analysis and Prediction

Explanation: Utilize diverse data sources like social media, government registers and medical files to evaluate and forecast the patterns of COVID-19 exposure by creating effective models.

Key Goals:

The outbreak patterns of COVID-19 ought to be evaluated.
Our research aims to anticipate trend patterns and subsequent epidemics.
We have to detect the determinant which leads to contagious diseases.

Research Methodology:

Data Collection: From government records, public health registers and social media data, we must collect data. To gather real-time data, make use of APIs.
Data Preprocessing: To normalize the diverse sources, separate the inappropriate details and manage the missing values, the data has to be cleaned and preprocessed.
Feature Engineering: Characteristics like population growth, weather scenarios, daily outbreak cases and isolation protocols need to be detected and developed.
Model Development: For anticipating the patterns, acquire the benefit of machine learning models such as LSTM and Random Forest, and time series analysis methods such as Prophet and ARIMA.
Assessment: By using metrics such as R-Squared, MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), contrast the performance of the model. With the aid of cross-validation or hold-out validation, we should assure the model.
Visualization: In order to visualize anticipations and trends, utilize tools such as Python’s Matplotlib or Tableau.

Anticipated Result:

For COVID-19 patterns, authentic prediction models could be developed.
Determinants which impact the disease transmission can be detected through this research.

Mining Social Media Data for Sentiment Analysis in Political Campaigns

Explanation: Regarding the political activities and applicants, interpret the people’s sentiment by evaluating data of social media.

Key Goals:

From social media posts, acquire the sentiment and preferences of people.
Periodically, it is required to monitor the modifications in public sentiment.
Sentiment patterns have to be integrated with political programs and results.

Research Methodology:

Data Collection: In accordance with political activities, gather the posts with the help of APIs from social media settings such as Reddit and Twitter.
Data Preprocessing: Eliminate special features, URLs and hashtags to clean the text data. Carry out stemming process and tokenization.
Feature Engineering: Deploy NLP (Natural Language Processing) methods to develop features like topic distributions, sentiment scores and word embeddings,
Sentiment Analysis: Use pre-trained models such as sentiment classifiers like TextBlob and VADER or BERT model to implement methods of sentiment analysis.
Time-Series Analysis: To detect the crucial scenarios which impact people’s opinion and evaluate sentiment patterns, time-series models must be executed by us.
Assessment: With annotated datasets, we should evaluate the sentiment analysis. The functionality of various techniques of sentiment analysis is meant to be contrasted.
Correlation Analysis: Employ statistical techniques such as Pearson correlation to connect sentiment trends with campaign activities.

Anticipated Result:

This research could offer perspectives into public sentiment and with political activities, it can be correlated.
Considering the political discussion, the main topics and consideration can be detected.

Anomaly Detection in IoT Networks Using Data Mining Techniques

Explanation: To detect the probable functional problems and security attacks, identify outliers in IoT networks through modeling efficient techniques.

Key Goals:

In IoT networks, focus on identification and classification of outliers.
The trends of regular and unusual activities must be detected.
As regards IoT systems, enhance the operational capability and security.

Research Methodology:

Data Collection: Encompassing the performance metrics, device logs and network traffic, we should gather data from IoT devices and sensors.
Data Preprocessing: Manage missing values, normalize the data and eliminate noise to clean and preprocess the data.
Feature Extraction: Features must be derived like sensor readings, network flow statistics and device consumption trends.
Anomaly Detection: To identify the outliers, unsupervised learning algorithms should be implemented such as One-Class SVM and Isolation Forest, and clustering algorithms such as DBSCAN and K-Means.
Classification: By using models such as SVM and Random Forest, categorize the outliers with the application of supervised learning methods. Deploy the model such as SVM and Random Forest.
Assessment: Utilize metrics such as F1-score, detection rate and false positive rate to evaluate the functionality of anomaly detection techniques.
Implementation: Real-time anomaly detection ought to be executed with the aid of stream processing frameworks such as Spark Streaming or Apache Flink.

Anticipated Result:

Generally in IoT networks, efficient identification and categorization of outliers can be accomplished.
Functional management and security could be improved in IoT systems.

Predictive Maintenance in Smart Manufacturing Using Data Mining

Explanation: In smart manufacturing platforms, this research enhances the maintenance programs and predicts the equipment breakdowns through designing predictive models.

Key Goals:

Ahead of time, we should anticipate the equipment breakdowns.
To decrease expenses and interruptions, the maintenance programs are supposed to be improved.
The capability and integrity of the production process is intended to be enhanced.

Research Methodology:

Data Collection: Incorporating operational logs, vibration and temperature, we have to collect sensor data from production equipment.
Data Preprocessing: Modify the noisy data, manage missing values and normalize the sensor feedback to clean the data.
Feature Engineering: Features must be developed like frequency elements of sensor readings, mean and variance. The main determinants of equipment condition should be detected.
Predictive Modeling: To develop predictive maintenance models, make use of machine learning algorithms such as LSTM, Gradient Boosting and Random Forest.
Model Assessment: Use metrics such as recall, F1-score, accuracy and precision to evaluate the functionality of the model. With baseline models, it must be contrasted.
Maintenance Scheduling: Depending on functional limitations and anticipated breakdowns, the maintenance programs need to be enhanced by creating effective techniques.
Deployment: Utilize environments such as Azure IoT or AWS IoT to implement models of predictive maintenance.

Anticipated Result:

Considering the equipment breakdowns, authentic anticipations can be determined.
For decreasing the maintenance expenses and interruptions, this study could enhance the maintenance programs.

Comparative Analysis of Clustering Algorithms for Customer Segmentation

Explanation: On the basis of purchasing records and populations, classify the consumers by conducting a detailed comparative analysis of various clustering techniques.

Key Goals:

It is required to detect the specific consumer groups.
The capability of different clustering techniques must be contrasted.
For intended marketing tactics, we should offer practical perspectives.

Research Methodology:

Data Collection: From retail industry or e-commerce environments, data must be collected by us on consumer activities, purchases and population statistics.
Data Preprocessing: We should address the missing values and normalize the attributes to clean and preprocess the data.
Feature Engineering: By using RFM (Recency, Frequency, and Monetary) analysis, characteristics ought to be developed such as product of choice, purchase frequency and average expenses.
Clustering Algorithms: Clustering techniques like Gaussian Mixture Models, k-Means, DBSCAN and Hierarchical Clustering needs to be executed.
Model Assessment: Adopt metrics such as cluster purity, Silhouette Score and Davies-Bouldin Index to assess the performance of clustering techniques.
Visualization: Deploy dimensionality reduction algorithms such as t-SNE and PCA to visualize clusters.
Comparison: For consumer classification, detect the most efficient techniques by contrasting the findings of clusters.

Anticipated Result:

Specific consumer groups can be detected through this research.
Particularly for individualized marketing, this research could offer perspectives into consumer activities.

Mining Electronic Health Records for Disease Prediction

Explanation: For preventive monitoring of disease and diagnosis, design predictive models through evaluating EHRs (Electronic Health Records).

Key Goals:

The possibility of health breakdowns aimed to be anticipated.
Crucial determinants which influence disease vulnerabilities are supposed to be detected.
Initial diagnosis and clinical results have to be enhanced.

Research Methodology:

Data Collection: Encompassing diagnostic codes, medical records and diagnostic codes, EHR data should be collected from healthcare settings.
Data Preprocessing: For cleaning and preprocessing the data, we must hide the patient details, normalize the medical conditions and manage the missing values.
Feature Engineering: Features are supposed to be extracted like diagnostic codes, patient demographics, lab outcomes, medical records.
Predictive Modeling: To forecast the susceptibility of diseases, employ the machine learning models such as Neural Networks, Random Forest, Logistic Regression and Decision Trees.
Model Assessment: Adopt metrics such as recall, ROC-AUC, accuracy and precision to evaluate the performance of models. In accordance with baseline frameworks, contrast our models.
Interpretation: In order to detect the main factors of risk and understand the model anticipations, we can deploy explainable AI methods.
Implementation: Primarily for evaluation of disease risk in real-time, predictive models are required to be executed in healthcare environments.

Anticipated Result:

This research could offer authentic prediction models for preventive detection of disease.
Care for patients can be enhanced and the critical factors of risk are detected through this study.

Sentiment Analysis of Product Reviews Using Deep Learning

Explanation: This project mainly intends to enhance the product design and analysis of consumer reviews. To evaluate the sentiments in product feedback, deep learning models are required to be created by us.

Key Goals:

Feedbacks about products are meant to be categorized as negative, positive and fair classes.
Main factors which influence sentiment of customer’s must be detected.
For product enhancement and customer experience, effective perspectives are supposed to be offered.

Research Methodology:

Data Collection: From online environments such as Yelp or Amazon, we must gather product feedback.
Data Preprocessing: Eliminate meaningless words to clean and preprocess the text data. Conduct the process of stemming and tokenization.
Feature Engineering: Take advantage of NLP methods to develop features like topic models, sentiment scores and word embeddings.
Deep Learning Models: For sentiment analysis, we need to implement the models such as BERT, CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory).
Model Assessment: By utilizing metrics such as F1-score, accuracy, precision and recall, the functionality of the model should be assessed.
Interpretation: To detect the main factors of sentiment and understand the model anticipations, deploy the visualization tools and attention mechanisms.
Execution: Especially for real-time feedback analysis, sentiment analysis models have to be implemented in e-commerce environments.

Anticipated Result:

On product feedback, this study can offer authentic sentiment classification.
Model perspectives could be contributed into product development sectors and customer experience.

Anomaly Detection in Financial Transactions Using Data Mining

Explanation: As a means to detect the probable fraud and inconsistencies, outliers are required to be identified in financial transactions through designing efficient techniques.

Key Goals:

Fraudulent or illegal financial transactions have to be identified and categorized.
Reliability and security of financial systems must be enhanced.
The financial losses that are caused through fraud have to be minimized.

Research Methodology:

Data Collection: Specifically from public datasets or financial companies, gather the transaction data.
Data Preprocessing: Manage the missing values, encrypt the predictive variables and normalize the amount of transaction to clean and preprocess the data.
Feature Engineering: Use field knowledge to derive characteristics such as location, time of day, transaction frequency and amounts.
Anomaly Detection: In transactions, identify the outliers by implementing methods such as Autoencoders, One-Class SVM and Isolation Forest.
Model Assessment Employ metrics such as F1-score, false positive rate and rate of detection to assess the models.
Interpretation: To interpret the features of illegal or unauthentic transactions, the identified anomalies must be evaluated.
Implementation: For real-time fraud detection in financial systems, anomaly detection models ought to be executed.

Anticipated Result:

Fraudulent transactions could be detected in an efficient manner.
Loss amounts might decrease and security can be improved.

Mining Educational Data for Predicting Student Success

Explanation: For student achievement, design predictive models by evaluating the academic data. A determinant which implicates educational performance is required to be detected.

Key Goals:

Student achievement and educational results are meant to be anticipated.
Main determinants which impact the performance of students are intended to be detected.
To enhance the educational approaches, we need to offer practical perspectives.

Research Methodology:

Data Collection: From academic institutions, the data should be collected on user participation, student population, attendance and academic achievements.
Data Preprocessing: For cleaning and preprocessing the data, we have to normalize the grades, manage missing values and encrypt predictive variables.
Feature Engineering: Features have to be designed such as prior academic functionality, involvement in knowledge enrichment programs, attendance rates and learning period.
Predictive Modeling: To anticipate scholar achievement, acquire the benefit of models such as Random Forest, Neural Networks, Logistic Regression and Decision Trees.
Model Assessment: Employ metrics such as ROC-AUC, precision, recall and accuracy to examine the model. With baseline frameworks, our model must be contrasted.
Interpretation: In order to detect the significant determinants which impact student achievement and understand the model anticipations, explainable AI methods have to be executed.
Deployment: Regarding the evaluation of student performance, predictive models need to be implemented in educational platforms.

Anticipated Result:

For student achievement, this research can develop authentic prediction models.
Major determinants which affect educational functionality could be detected.

Comparative Study of Recommender Systems for Personalized Content Delivery

Explanation: For the purpose of distributing the customized content to users, this project detects the most efficient technique by means of evaluating and contrasting diverse techniques of the recommender system.

Key Goals:

Techniques of the recommender system need to be designed and contrasted.
Especially for customized content distribution, highly productive techniques are meant to be detected.
User participation and experience should be enhanced.

Research Methodology:

Data Collection: From content environments such as YouTube or Netflix, user communication data are required to be gathered.
Data Preprocessing: Encrypt the predictive variables, manage the missing values and normalize the user feedback to clean and preprocess the data.
Feature Engineering: We should develop features such as content metadata, interaction records and consumer opinions.
Recommender Algorithms: Techniques have to be implemented such as Hybrid Methods, Collaborative Filtering, Matrix Factorization and Content-Based Filtering.
Model Assessment: By using metrics such as MAE (Mean Absolute Error), MRR (Mean Reciprocal Rank), Recall@K and Precision@K, we can assess the functionality of the model.
Comparison: To detect highly-efficient techniques, the performance of various recommender techniques must be contrasted.
Deployment: For customized content distribution in real-time, recommender systems ought to be executed in content environments.

Anticipated Result:

This study could offer efficient distribution of customized content.
It can enhance the user experience and participation.

What are some great final year project ideas in Data Mining, NLP, Machine Learning Data Analytics, for a Btech CSE STUDENT?

Regarding the current scenarios, the domains like Machine Learning, NLP (Natural Language Processing), Data Mining and Data Analytics are widely considered among research people for impactful projects. In addition to short explanations and recommended methodologies, some of the compelling project concepts are suggested by us across these areas that are suitable for Btech CSE STUDENT who are willing to carry out their final year project:

Real-Time Sentiment Analysis on Social Media Data

Explanation: Observe and evaluate the sentiments from social media environments like Facebook or Twitter by creating a model of real-time sentiment analysis. For interpreting the people’s opinion on diverse topics, brand monitoring and risk management, this system is highly beneficial.

Main Elements:

Goal: In real-time, public sentiment has to be observed and evaluated.
Required Tools: Python includes TensorFlow, NLTK and TextBlob and for real-time data streaming, make use of Apache Kafka.
Datasets: Reddit comments and Twitter API data.

Research Methodology:

Data Collection: From social media, gather actual time data with the help of APIs.
Data Preprocessing: We must normalize the text; eliminate the meaningless words, and specific features for cleaning and preprocessing the text data.
Feature Engineering: Characteristics need to be derived like topic modeling and sentiment scores.
Model Development: Use pre-trained models or LSTM and BERT to execute models of sentiment analysis.
Assessment: With the help of metrics such as F1-score, accuracy, recall and precision, the performance of the model has to be evaluated.
Implementation: Apply the real-time data processing model such as Apache Flink or Apache Kafka to implement the model.
Predictive Maintenance in Smart Manufacturing

Explanation: Considering smart manufacturing, the maintenance programs have to be enhanced and anticipate the equipment breakdowns by modeling a predictive maintenance system which effectively utilizes sensor data.

Main Elements:

Goal: To decrease maintenance expenses and interruptions, equipment breakdowns are required to be anticipated.
Required Tools: Apache Spark for big data processing and Python involves TensorFlow and Scikit-learn.
Datasets: From industrial IoT sensors, acquire the public datasets like NASA Prognostics Data Repository.

Research Methodology:

Data Collection: Specifically from production machines, sensor data must be collected.
Data Preprocessing: Address the anomalies and missing values to clean and preprocess the data.
Feature Engineering: We should derive characteristics like operational metrics, vibration and temperature.
Model Development: Deploy machine learning algorithms such as Neural Networks, Random Forest and Gradient Boosting to design predictive models.
Assessment: Utilize metrics such as RMSE (Root Mean Squared Error) and MAE (Root Mean Squared Error) to evaluate the performance of the model.
Deployment: To offer alert messages for predictive maintenance, the model has to be synthesized with a real-time monitoring system.
Customer Segmentation for Targeted Marketing

Explanation: According to demographic data and purchasing activities, this research classifies the customer into specific groups through developing a customer segmentation model. For developing the intended marketing tactics, this model is very essential.

Main Elements:

Goal: For customized marketing, focus on detection of specific consumer groups.
Required Tools: Tableau for data visualization and Python involves Pandas and Scikit-learn.
Datasets: Particularly for user-specific data and Kaggle, execute the E-commerce transaction data.

Research Methodology:

Data Collection: Consumer purchase records and population data ought to be accumulated.
Data Preprocessing: To assure stability, the data must be cleaned and preprocessed.
Feature Engineering: Use RFM analysis to develop properties such as product opinions, average expenses and purchase frequency.
Model Development: Clustering techniques are required to be executed such as DBSCAN, k-Means and Hierarchical Clustering.
Assessment: Use metrics such as Davies-Bouldin Index and Silhouette Score to assess the capacity of the cluster.
Visualization: Exhibit the segmentation findings by using data visualization tools.
Anomaly Detection in Financial Transactions

Explanation: In financial data, detect the unauthentic transactions by generating an anomaly detection system. Abnormal patterns which reflect illegal activity are supposed to be detected through this system.

Main Elements:

Goal: Fraudulent transactions should be identified and categorized.
Required Tools: Apache Spark and R for big data processing ,and Python involves PyCaret and Scikit-learn
Datasets: Specifically from Kaggle, use the dataset of credit card fraud detection.

Research Methodology:

Data Collection: From financial entities or public datasets, financial transaction data is meant to be collected.
Data Preprocessing: The data has to be cleaned and preprocessed by normalizing the data and handling the missing values.
Feature Engineering: We need to derive characteristics like geographic location, transaction amount and frequency.
Model Development: Methods of outlier detection ought to be adopted such as Autoencoders, One-Class SVM and Isolation Forest.
Assessment: By using metrics such as F1-score, detection rate and false positive rate, we must evaluate the functionality of the model.
Implementation: For real-time fraud monitoring, anomaly detection system is intended to be executed.
Text Summarization for News Articles

Explanation: Develop short outline of news articles by designing an efficient model of text summarization. The significant points of extended articles are easily interpreted by users through this system.

Main Elements:

Goal: News articles have to be outlined in an automatic manner.
Required Tools: PyTorch and Python involve Hugging Face Transformers and NLTK (Natural Language Toolkit).
Datasets: From different sources such as BBC or CNN/Daily Mail, accumulate the datasets of news articles.

Research Methodology:

Data Collection: News reports ought to be gathered by us from news websites or public datasets.
Data Preprocessing: Eliminate the useless details and noise to clean and preprocess the text data.
Feature Engineering: To retrieve significant sentences and phrases, acquire the benefit of NLP (Natural Language Processing) methods.
Model Development: With the aid of methods such as Transformer models, BERT or TextRank, we must execute the models of text summarization.
Assessment: Utilize metrics such as BLEU and ROUGE scores to evaluate the concise features.
Implementation: For text summarization in real-time, a mobile app or web interface is meant to be designed by us.
Sentiment Analysis for Customer Reviews

Explanation: To evaluate the customer feedback, this research effectively generates a sentiment analysis system.

Main Elements:

Goal: Feedbacks of customers have to be categorized into sentiment categories.
Required Tools: RapidMiner and Python involve TensorFlow, TextBlob and NLTK (Natural Language ToolKit).
Datasets: Through online settings such as Yelp or Amazon, gather consumer feedback.

Research Methodology:

Data Collection: From analysis platforms or e-commerce settings, feedback of consumers should be collected.
Data Preprocessing: We have to eliminate meaningless words and punctuation to clean the text data, Conduct the process of tokenization.
Feature Engineering: Features must be designed such as word embeddings, topic models and sentiment scores.
Model Development: For sentiment classification, take advantage of deep learning models such as LSTM, and machine learning models such as SVM and Logistic Regression.
Assessment: Adopt metrics such as recall, F1-score, precision and accuracy to assess the model performance.
Implementation: To evaluate and exhibit the sentiment patterns, an effective application should be generated.
Recommendation System for E-commerce

Explanation: According to searching and purchase records of customers, a recommendation system should be modeled by us that recommends suitable products for consumers.

Main Elements:

Goal: To improve the customer experience in shopping, preferable products are supposed to be suggested by us through a recommender system.
Required Tools: Apache Mahout and Python involve Scikit-learn and Surprise.
Datasets: From online settings like Amazon, accumulate the e- commerce transaction data.

Research Methodology:

Data Collection: As regards product descriptions and customer transactions, we must accumulate data.
Data Preprocessing: Normalize the characteristics and address the missing values to clean and preprocess the data.
Feature Engineering: It is required to develop features like purchase records, user-product interactions and ratings.
Model Development: Recommendation techniques such as Matrix Factorization, Collaborative Filtering and Content-Based Filtering need to be executed.
Assessment: Use metrics such as MAE (Mean Absolute Error), precision@k and recall@k to access the performance of models.
Implementation: In an e-commerce environment, the recommendation system must be executed.
Predictive Analytics for Student Performance

Explanation: On the basis of their attendance, academic registers and other determinants, a predictive model is meant to be developed for predicting the performance of students.

Main Elements:

Goal: Emphasize on the performance of students. The factors which impact educational achievement are aimed to be detected here.
Required Tools: R and Python include TensorFlow and Scikit-learn.
Datasets: Gather the academic datasets like UCI Student Performance Dataset.

Research Methodology:

Data Collection: Incorporating population data, ranks and attendance of students, we have to collect data.
Data Preprocessing: For managing the missing values, the data is meant to be cleaned.
Feature Engineering: We should derive characteristics like prior academic achievements, involvement in knowledge enrichment activities and learning periods.
Model Development: To forecast the performance of students, make use of machine learning techniques such as Neural Networks, Logistic Regression and Decision Trees.
Assessment: By using metrics such as ROC-AUC, accuracy, recall and precision, the performance of the model is supposed to be analyzed.
Deployment: Offer evaluation of real-time performance through implementing the predictive model in an educational platform.
Comparative Analysis of Clustering Algorithms for Market Segmentation

Explanation: In accordance with consumer data, detect highly efficient techniques for classifying markets through conducting detailed comparative analysis of various clustering techniques.

Main Elements:

Goal: For intended marketing tactics, focus on detection of specific customer groups.
Required Tools: Tableau for data visualization, R and Python involves Pandas and Scikit-learn.
Datasets: From e-commerce or retail environments, collect the data of demographics and consumer purchase.

Research Methodology:

Data Collection: Depending on characteristics, population and purchases, we have to gather the consumer data.
Data Preprocessing: For stability purposes, the data must be cleaned and preprocessed.
Feature Engineering: We should develop characteristics such as product opinions, average expenses and purchase frequency.
Clustering Algorithms: Specific techniques ought to be executed such as Gaussian Mixture Models, Hierarchical Clustering, DBSCAN and k-Means.
Model Assessment: By adopting Davies-Bouldin Index and Silhouette Score, the capabilities of clusters are intended to be assessed.
Visualization: To exhibit the findings, deploy significant tools of data visualization.
Real-Time Data Analytics for Smart Cities

Explanation: In order to enhance urban management and facilities, this study observes and evaluates data from diverse sources in a smart city by creating efficient systems of real-time data analytics.

Main Elements:

Goal: As a means to enhance city services and models, real-time data is required to be evaluated.
Required Tools: Apache Flink for real-time data processing, Python includes Scikit-learn and Pandas, and Apache Kafka.
Datasets: Especially from smart city architectures like energy consumption, traffic sensors and air quality monitors, gather the sensor data.

Research Methodology:

Data Collection: From IoT devices and city sensors, real-time data must be collected by us.
Data Preprocessing: To manage missing values and noise, the data needs to be cleaned and preprocessed.
Feature Engineering: Characteristics like energy usage, traffic flow and pollution level should be extracted.
Model Development: Use machine learning methods such as Neural Networks, Gradient Boosting and Random Forest for executing the predictive models.
Assessment: Metrics are supposed to be implemented such as recall, accuracy and precision to assess the functionality of the model.
Implementation: For the purpose of tracking and enhancing the city services, we have to design a real-time analytics system in an effective manner.

Recent Research Ideas in Data Mining

Recent Research Ideas in Data Mining that are circulating in scholar’s world are shared by us. Explore some of the, significant areas like Data Mining, Data Analytics, NLP and Machine Learning have evolved rapidly and contribute innovative advancements to the modern platforms. Among these fields, we provide numerous research topics along with significant elements and research methodologies. Some get in touch with us to get tailored research solutions from writing to publication.

Association rules data mining in manufacturing information system based on genetic algorithms
Evolutionary molecular structure determination using Grid-enabled data mining
The Research about the Application of Data Mining Technology Based on SLIG in the CRM
Data mining with self generating neuro-fuzzy classifiers
Data mining for constructing ellipsoidal fuzzy classifier with various input features using GRBF neural networks
Enhance Software Quality Using Data Mining Algorithms
Exploiting Data Mining Techniques for Improving the Efficiency of a Supply Chain Management Agent
Applications of unsupervised neural networks in data mining
An Effective Data Mining Technique for the Multi-Class Protein Sequence Classification
Content clustering of Computer Mediated Courseware using data mining technique
A systematic method to design a fuzzy data mining model
The research of customer loss question of logistics enterprise based on the data mining
The Application of Data Mining in Mobile Subscriber Classification
Data Mining of Corporate Financial Risks: Financial Indicators or Non-Financial Indicators
Decision Tree based Routine Generation (DRG) algorithm: A data mining advancement to generate academic routine for open credit system
An improved fusion algorithm based on RBF neural network and its application in data mining
Optimization model for Sub-feature selection in data mining
An Imbalanced Big Data Mining Framework for Improving Optimization Algorithms Performance
Research on Web Components Association Relationship Based on Data Mining
Clustering techniques in data mining: A comparison
Data mining for the optimization of production scheduling in flexible Manufacturing System
Simulation Data Mining for Functional Test Pattern Justification
Experiments in applying advanced data mining and integration to hydro-meteorological scenarios
A review on knowledge extraction for Business operations using data mining
Role of Educational Data Mining and Learning Analytics Techniques Used for Predictive Modeling
Research on GIS-based spatial data mining
Evaluation Method of Immersive Drama Stage Presentation Effect Based on Data Mining
Software defect detection by using data mining based fuzzy logic
Malware analysis using reverse engineering and data mining tools
Data mining for understanding and improving decision-making affecting ground delay programs