The extraction of useful data from raw data is known as “data mining”. This is mainly used in the different industries while collecting information from multiple sources. For this extraction, various mining tools were introduced already and still developing. These tools are differentiated by their characteristics and purposes. 

This page describes you technical information that is necessary for the research and development of data mining projects on GitHub!!!

One common thing is pattern identification from large-scale data for data processing to meet specific business needs. For instance: decision-making, marketing plans creation, increased sales, and production. For developing data mining projects GitHub plays a supportive role. It is a web-oriented framework used for codebases hosting and project version management. 

Data mining projects github research guidance

What do you mean by data mining?

Data mining is the key technology for filtering and processing useful information over a huge volume of raw data. In specific, the data can be collected from multiple sources in any format like audio, image, video, text, etc. Mainly, it uses different software merely to perform processes like data collection, important pattern (data) identification, pattern analysis, and many more for grabbing new data. Therefore, alternatively, it is referred to as knowledge discovery in raw information.

What are the types of data mining? 

As mentioned earlier, data mining is utilized to mine useful information from large data. Most importantly, it has a procedure to mine knowledge from data. Some important mining techniques are 

  • Prediction
  • Outlier detection
  • Regression
  • Sequential patterns
  • Clustering
  • Association rules   

For illustration purposes, now we can see about the manufacturing industries. When data mining technology is imported into research and development, it is required to find the emerging needs. As well, they are recognizing the origin of manufacturing issues, identifying new products arrival, profiling customer, cross-selling to present customers, preventing customer attrition, etc. 

Our research team usually collects more information on new research areas of Data Mining Projects Github. As well, we also refer to many research magazines, articles, and papers of reputed journals such as IEEE, emerald, Springer, ScienceDirect, etc. This makes our team more updated in recent research areas. Further, we also specified to you the important research perspectives of each area. To know other thought-provoking research areas of data mining, connect with us.

Data Mining Research Areas 

  • Processing of Learned Knowledge
    •  Inference and Data Analysis 
    • Statistical Approaches for Efficient Mining
    • Mining Risks, Interfaces, Opportunities, and Languages 
    • Reliable Data Model for New Mining Trends 
    • OLAP-based Data Warehousing and Mining Integration 
    • Data Mining from Low-quality Sources
    • KDD-based Knowledge and Constraints Integration
    • Cooperative Data Study, Learning and Visualization 
    • Knowledge-based Discovery and Representation (pre and post-processing)
  • Data Mining Enhancements 
    • Secure Data Hiding 
    • Spatial Data Mining
    • Web and Graph Mining
    • Pre-processing and Visualization Approaches
    • Extraction of Data Streams
    • Multimedia Mining (Text, Image, and Video)
    • Data Mining Techniques (Distributed and Parallel)
  • Applications of Data Mining 
    • Image Analysis
    • Data Mining Learning
    • Databases
    • Bioinformatics and Biometrics
    • Data Clustering and Classification
    • Financial Design and Prediction
    • Social Media Comment Analysis in Social Networks

In addition, we have also given you the latest research trends of data mining. When we collect recent research areas, we also update our skills on the latest trends in data mining projects github. We assure you that these trends are collected from top-research areas. Also, these trends guarantee the highest degree of future scope. Since the selection of research areas/ideas without the future scope will have limited research references and materials. So, we always select unique and futuristic research topics. In this way, we have recognized the following ideas as trend-setter for future technologies of data mining.

Data Mining Trends and Research Frontiers

  • Mining Tasks Controlling Systems 
  • Natural Language Processing
  • Ontology Design, Mapping, and Analytics
  • Knowledge-assisted Models
  • New Knowledge Findings in Data Mining
  • Intensive-Data Management Systems
  • Decision-Making Support for Expert Systems 
  • Knowledge Design, Representation, and Maintenance

Now, we can see the main objectives of data mining projects Github. Through significant methodologies, one can learn and extract different knowledge from more complicated / mixture datasets. Our developing experts have come to crossed various datasets and successfully performed different processes for knowledge discovery. For your information, here we have given you the key processes which explicitly present you the workflow of a data mining project. All these algorithms and techniques work effectively on raw datasets. 

Data Mining Process, Algorithms, and Techniques

  • Information preprocessing
    • Cleaning of Information 
      • Lost Values Extraction
        • Attribute Mean-based Technique
        • Tuple Elimination
        • Probable Value Technique
        • Global Constant Technique
    • Noise-Data Removal 
      • Knowledge Analytical Technique
      • Computer-based Analysis Technique
      • Binning Technique
  • Transformation of Information 
    • Discretization
    • Data Generalization and Aggregation
    • Smoothing and Normalization
    • Hierarchical Model Generation
  • Reduction of Information 
    • Data Compression
    • Dimensionality Reduction
    • Data Cube Aggregation
  • Selection of Attributes / Features
    • Mutual Information
    • Optimization Techniques 
      • Ant Lion Colony Optimization 
      • Whale Optimization
      • Spider Monkey Optimization
    • Correlation-based Optimal Attribute Subset Selection 
  • Analysis of Information 
    • Creation of Cluster 
      • DBSCAN
      • K-means++
      • Fuzzy Sets-based clustering
      • Transitive heuristic algorithm
      • Gaussian mixture-based Expectation-Maximization 
  • Classification
    • Deep Learning 
      • LSTM
      • DBN
      • CNN
      • DNN
    • Machine Learning 
      • Naïve Bayes
      • PCA 
      • SVM 
      • ICA
      • ANN
      • Decision trees
    • Regression
      • Lasso Regression
      • Multivariate Regression
      • Least Square Regression
      • Multiple Regression
      • Logistic Regression

In the above section, we have already seen the major research areas of data mining. To the continuation, here we have given the other significant research areas of data mining. Once you communicate with us, we provide you with more information and research topics on any of your interested areas from the below lists. Further, we have also supported you in other emerging research areas of data mining projects github. Our ultimate goal is to give you an end-to-end development service with the latest research topics for any kind of research area.

Top 10 Interesting Data Mining Research Topics 

  • Recurrent Itemset Extraction
  • Text / Image Search Systems
  • Artificial Intelligence in Mining
  • Natural Language Processing (NLP)
  • Social Network Analysis
  • Internal / External Anomaly Detection 
  • Custom-based Product Recommender 
  • Web Mining for Semantic Analytics
  • Comment-based Sentiment Analysis
  • Bio-Signal Collection and Medical Diagnosis

Next from the development point-of-view, here we have given you a few reliable tools for implementing the best result-yielding data mining projects GitHub. We ensure you that all these tools are furnished with effective development platforms, libraries, toolboxes, packages, etc. Our developers suggest you best-fitting development tools for your project. For this selection, we have certain criteria like research objectives, tools facilities, results accuracy, developer-friendliness, etc. There, we are here to develop your handpicked ideas and clarify your doubts in project development.

Tools for data mining projects GitHub repository
  • Weka
  • Rattle
  • NLTK
  • Orange
  • XL Miner
  • Knime
  • Rapid Miner 
  • Tanagra
  • R-programming

Sample PhD Project in Data Mining “Supply Chain Management”

For more clarity, our developers have given you one sample project as “supply chain management”. In this project, we have suggested the main model as Four-Tier Green Supply Chain Model and Purchase Intent Prediction (FT-GSCMPIP). Our proposed model is surely intended for large-scale data. 

To support large-scale data, we used HDFS big data analytics for storing and processing large-scale data. As well, we proposed strong, quick processing, and low complexity techniques. Below, we have also given you the key components of the four-tier supply chain model.

Four-Tier Supply Chain Model 

  • Suppliers 
  • Distributors 
  • Manufacturers 
  • Customers 
  • Government (Manager and Board Members)

Product Need and Selling Price Valuation (Tier-1)

  • Financial and recurrent environ variation threats influence the prediction of demand rate
  • For every product, the environ threats and customer behavior are used as major factors
  • Predict the optimized product’s selling price which uses products purchase and performance histories
    • Fuzzy-Search Rescue Optimizer

Selection of Optimal Supplier for Manufacture (Tier-2)

  • Use KPI’s history to determine the best supplier concerning performance index and supplier criteria
    • MCDM techniques for real-time scenarios
  • Select one factor as base as either preferential or selective
  • Then, determine other factors based on base-factor
  • Next, perform pair-wise comparison where comparison counts are lower than other techniques
Top 10 Research Data Mining Projects github

Selection criteria for Supplier Selection

  • Price
    • Service 
    • Product 
    • Shipping 
  • Quality of Product
    • Service Contract Time 
    • Qualification Rate of Raw material
  • Packaging     
    • Hard to Wear
    • Simple to Open 
    • Green Packing 
  • Time
    • Shipping 
    • Processing 
    • Distribution 
  • Shipping / Delivery Rate     
    • Attained Distance
    • On-time Distribution 
  • Service 
    • The ratio of Consumer Satisfaction

Selection of Group of Distributors (Tier-3)

  • Use the following factors to determine the group of distributors
    • Stock Rate
    • Reception Rate
    • Environment Threats
    • Delivery Rate
    • Customer Readiness
    • Service Rate of Customer 
    • Price Variation Rate 
    • Use Bipartite MapReduce Matching (BMM) for distributor’s selection where processing time is low than other techniques

Estimation and Sanction of Customers Purchase (Tier-4)

  • Forecast the intention of customer prediction by following parameters with Resilient-GRU
    • Demographics
    • History
    • Purchase 
  • Classify the intention of purchase into the following 6 categories
    • Unsure
    • Determined
    • Not Determined
    • Strongly Intent
    • Strongly Not Intent
    • Somewhat Intent
  • Assess the performance of the whole system by following parameters  
    • Recall
    • F-Measure 
    • Precision 
    • ROC Curve 
    • AUC Curve 
    • Number of Suppliers Versus Ranking 
    • Number of Suppliers Versus Delivery Reliability 

On the whole, we are here to support you in every aspect of research and code execution Data Mining Projects GitHub. In particular, we also provide you with details about the technological developments of the current data-mining study. We ensure you that we will be with you till you reach your project destination. Therefore, connect with us to create a masterpiece of the project in your data mining using Python researchjourney. Also, we assure you that our project quality satisfies your needs in all aspects at the time of project delivery.