- All
- Engineering
- Data Engineering
Powered by AI and the LinkedIn community
1
Data preprocessing
2
Data classification
3
Data clustering
4
Data visualization
5
Data ethics
6
Data mining techniques for sustainability and social impact
7
Here’s what else to consider
Data mining is the process of discovering patterns, trends, and insights from large and complex data sets. It can help solve various problems and challenges in sustainability and social impact, such as reducing environmental impact, improving health and education, and promoting social justice. However, data mining also requires careful planning, execution, and evaluation to ensure its effectiveness and ethicality. In this article, you will learn about some of the most effective data mining techniques for sustainability and social impact, and how to apply them in your data engineering projects.
Top experts in this article
Selected by the community from 38 contributions. Learn more
Earn a Community Top Voice badge
Add to collaborative articles to get recognized for your expertise on your profile. Learn more
- Hari Krishnan Department of Artificial Intelligence and Data Science at RAMCO INSTITUTE OF TECHNOLOGY
10
- Archit Singh Seeking 2024 full-time role | Operations Analyst @ Q2 | MS-MIS student @ TAMU - Mays Business School | Ex-Wipro
6
-
4
1 Data preprocessing
Before you can apply any data mining technique, you need to go through the process of data preprocessing. This involves cleaning, transforming, and integrating your data to make it suitable for data mining. Data preprocessing can help you improve the quality and reliability of your data, as well as reduce its complexity and dimensionality. Common techniques include data cleaning, which involves removing or correcting missing, noisy, or inconsistent data; data transformation which is scaling, normalizing, or standardizing your data; data integration which combines data from different sources; data reduction which selects, aggregates, or samples your data; and finally, data discretization which converts continuous or numerical data into discrete or categorical data.
Help others by sharing more (125 characters min.)
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data ethics considerations are essential in all stages of data mining, including data collection, analysis, and interpretation. Ensuring ethical data practices, such as privacy protection and bias mitigation, is critical for maintaining trust and integrity in sustainability and social impact initiatives.
LikeLike
Celebrate
Support
Love
Insightful
Funny
4
- Hari Krishnan Department of Artificial Intelligence and Data Science at RAMCO INSTITUTE OF TECHNOLOGY
(edited)
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Preprocessing entails preparing the dataset prior to actual analysis. Common tasks include ensuring accurate exportation of the dataset, organizing it into appropriate tables, and formatting it correctly for further analysis.
LikeLike
Celebrate
Support
Love
Insightful
Funny
6
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
In my experience, data preprocessing serves as the cornerstone of effective data mining. Given the influx of unstructured and unformatted streaming data, its significance has skyrocketed. Investing time and attention into this step is crucial to avoid errors in later analyses. Emphasizing the importance of data preprocessing not only ensures accuracy but also establishes a proactive approach, strengthening the foundation for sound decision-making.
LikeLike
Celebrate
Support
Love
Insightful
Funny
2
- Nishkarsh Gupta MS-ISM @ Arizona State University | SQL, Python, and Tableau | Actively Seeking Data/Business Analyst and Data Scientist Roles
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data classification stands out as a powerful tool for sustainability and social impact due to its ability to organize data into specific categories. This organization enables organizations to pinpoint where and how to direct their efforts most effectively, such as focusing conservation activities on areas heavily impacted by deforestation. It also allows for the prediction of future trends, like pollution levels, aiding in proactive measures. Furthermore, classification aids in allocating resources efficiently to those in dire need or who could benefit the most, optimizing the impact of interventions in areas such as healthcare and education.
LikeLike
Celebrate
Support
Love
Insightful
Funny
2
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data mining aids sustainability and social impact. Techniques include predictive modeling, clustering, association rules, text analysis, and graph mining.1. Predictive Modeling: Forecast trends in sustainability and social impact.2. Cluster Analysis: Identify patterns and segments in communities or ecological regions.3. Association Rule Mining: Uncover relationships between variables for informed decision-making.4. Text Mining and Sentiment Analysis: Extract insights from social media, news, and surveys for public sentiment.5. Graph Mining: Analyze social networks or ecological systems for relationships and dependencies.
LikeLike
Celebrate
Support
Love
Insightful
Funny
1
Load more contributions
2 Data classification
Data classification is a data mining technique that assigns labels or categories to your data based on predefined rules or criteria. This technique can help identify, group, or separate data into meaningful and useful classes, and can be used for various purposes in sustainability and social impact such as predicting outcomes, detecting anomalies, recommending actions, and evaluating the performance of policies or programs. Common data classification algorithms include decision trees, k-nearest neighbors, support vector machines, and Naive Bayes. Decision trees split data into branches based on rules or conditions, k-nearest neighbors use the distance or similarity between data and its neighbors to assign labels or categories, support vector machines use a hyperplane or boundary to separate data into different classes or groups, and Naive Bayes uses probabilities and statistics to calculate the likelihood of data belonging to a certain class or group.
Help others by sharing more (125 characters min.)
- Hari Krishnan Department of Artificial Intelligence and Data Science at RAMCO INSTITUTE OF TECHNOLOGY
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data classification is broadly outlined as the procedure of arranging data into pertinent categories to optimize its utility and safeguard it more effectively. Essentially, this process streamlines data retrieval and location. Its importance is particularly evident in risk management, compliance, and data security.The data classification process involves tagging data to enhance its searchability and accessibility. It also eliminates the duplication of data, resulting in decreased storage and backup costs, while concurrently expediting the search process. Despite the perception of being highly technical, understanding this facet is essential for organizational awareness.
LikeLike
Celebrate
Support
Love
Insightful
Funny
10
- Jerome Jayanathan Needhipathi Delivery Lead @ Shell | Doctorate in Business Administration - specializing in AI Governance.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
In sustainability, data classification aids in species conservation. Using decision trees, features like habitat type, climate, and human interference classify endangered species habitats. K-nearest neighbors identify similar habitats based on proximity. Support vector machines discern habitats needing urgent protection by drawing boundaries based on critical factors. Naive Bayes assesses probabilities of habitat suitability for species. This classification helps prioritize conservation efforts, directing resources to habitats most in need, ensuring effective biodiversity preservation and positive social impact.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- Santhosh Nalgonda IT Software Engineering Specialist at BCG l 6k Plus Followersl Ex ZS I Backend Developer Enabling GenAI l AWS Certified Architect- Analytics
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data classification is a fundamental task in machine learning and data analysis, involving the categorization of data into predefined classes or groups based on certain features or characteristics. This process is crucial for making predictions, deriving insights, and facilitating decision-making. Key aspects of data classification include:1. **Training Data:** - Utilizing a labeled dataset for training a classification model. Labeled data consists of instances with known outcomes or classes.2. **Feature Selection:** - Identifying relevant features or attributes that contribute to distinguishing between different classes. Feature selection is essential for model accuracy.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- An Phan Data Engineer | AI Solutions Engineer at AITOMATIC, Inc.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Categorize data into classes based on criteria, useful for tasks like identifying energy consumption patterns or predicting social behaviors.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- Vijay S.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Classification involves identifying the category an item belongs to. This can be used in sustainability to categorize types of waste for more efficient recycling processes, or in social impact to classify communities based on vulnerability or need levels, facilitating more tailored aid and development programs.
LikeLike
Celebrate
Support
Love
Insightful
Funny
Load more contributions
3 Data clustering
Data clustering is a data mining technique that groups your data into clusters or segments based on their similarity or dissimilarity. It can be used to uncover hidden patterns, structures, or relationships in your data that are not obvious or predefined. Data clustering can help you gain insights, understand the characteristics of different groups or individuals, identify potential causes or effects, and enrich your data with additional information. Common algorithms for data clustering include K-means, Hierarchical clustering, Density-based clustering, and Model-based clustering. K-means uses centroids to partition your data into clusters based on their distance or proximity. Hierarchical clustering utilizes a tree-like structure to merge or split your data into clusters based on their similarity or dissimilarity. Density-based clustering forms clusters based on the density or concentration of your data and its connectivity or isolation. Lastly, Model-based clustering fits your data into clusters based on their likelihood or fit.
Help others by sharing more (125 characters min.)
- Vladimir Kobzev Product Manager @ Improvado | PMBOK Certified
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
i love gephy for the task. You can beautifully clustering datait's opensource and easy to use. You can check gephy community if any questions
LikeLike
Celebrate
Support
Love
Insightful
Funny
1
- Jerome Jayanathan Needhipathi Delivery Lead @ Shell | Doctorate in Business Administration - specializing in AI Governance.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
data clustering aids in urban planning for eco-friendly cities. Using K-means, data on population density, green spaces, and energy usage cluster neighborhoods into categories like residential, commercial, or industrial zones. Hierarchical clustering groups similar neighborhoods based on environmental impact, guiding resource allocation. Density-based clustering identifies regions with high environmental stress due to population density and pollution. Model-based clustering assesses the likelihood of neighborhoods adopting sustainable practices. This clustering facilitates targeted interventions for sustainable urban development, promoting eco-friendly policies and improving social well-being.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- Santhosh Nalgonda IT Software Engineering Specialist at BCG l 6k Plus Followersl Ex ZS I Backend Developer Enabling GenAI l AWS Certified Architect- Analytics
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data clustering is a unsupervised machine learning technique that involves grouping similar data points into clusters based on inherent patterns or similarities. Unlike classification, clustering doesn't require labeled data, making it particularly useful for exploratory data analysis. The process involves identifying the natural structure within a dataset, where data points within the same cluster share common characteristics or features. Various algorithms, such as K-means, hierarchical clustering, and DBSCAN, are employed to partition the data into clusters.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- An Phan Data Engineer | AI Solutions Engineer at AITOMATIC, Inc.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Identify similar data groups to reveal community-level trends, aiding in pinpointing shared environmental concerns or common consumer preferences.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- Vijay S.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
This technique involves grouping sets of data points so that those within each group are more closely related to one another than to those in other groups. Clustering can be used to identify areas with similar sustainability challenges, such as regions with high levels of pollution or communities with similar social issues, enabling targeted interventions.
LikeLike
Celebrate
Support
Love
Insightful
Funny
4 Data visualization
Data visualization is the process of presenting your data in graphical or visual forms, such as charts, graphs, maps, or images. It can be used to communicate, interpret, and understand your data in a more effective and engaging way. For sustainability and social impact purposes, data visualization can be used for reporting, summarizing, explaining, illustrating, comparing, contrasting, persuading, and influencing your data. Common data visualization tools and techniques include bar charts (horizontal or vertical bars to show frequency or magnitude), pie charts (circular sectors to show proportion or percentage), line charts (points and lines to show trends or changes over time), and scatter plots (dots or markers to show relationships or correlations between two variables).
Help others by sharing more (125 characters min.)
- Archit Singh Seeking 2024 full-time role | Operations Analyst @ Q2 | MS-MIS student @ TAMU - Mays Business School | Ex-Wipro
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
In my experience, data visualization significantly improves decision-making by distilling complex data into clear insights. Customized visualizations enhance communication and support for sustainability initiatives, making information more accessible and impactful. Overall, data visualization is a powerful tool for driving informed decisions and meaningful social impact.
LikeLike
Celebrate
Support
Love
Insightful
Funny
6
- Hari Krishnan Department of Artificial Intelligence and Data Science at RAMCO INSTITUTE OF TECHNOLOGY
(edited)
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data scientists have two main goals: figuring out useful information from data and explaining those findings to people who might not know much about technical stuff.When we show data to others, it can be hard because not everyone understands technical terms. That's why using pictures or charts is helpful. Here are some types:1. Line Chart2. Bar Chart3. Scatter Plot4. Area Plot and Stacked Plot5. Pie Chart6. Table Chart7. Polar Chart8. Histogram9. Lollipop Chart
LikeLike
Celebrate
Support
Love
Insightful
Funny
5
- Temitope Adeyeha Data Engineering | ML Research
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
In the realm of sustainability and social impact, data visualization transcends mere representation—it becomes a catalyst for awareness and change. Visualization techniques are pivotal in conveying complex environmental and societal data to diverse audiences, fostering understanding and engagement. Compelling visual narratives unveil patterns, making data relatable and actionable. A well-crafted visualization serves as a bridge, connecting data-driven insights with stakeholders, policymakers, and the general public. It transforms raw information into a compelling story, empowering individuals to grasp the urgency of sustainability challenges and actively participate in social impact initiatives.
LikeLike
Celebrate
Support
Love
Insightful
Funny
1
- Jerome Jayanathan Needhipathi Delivery Lead @ Shell | Doctorate in Business Administration - specializing in AI Governance.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Consider visualizing renewable energy adoption. A line chart shows how much renewable energy we use has increased each year, pointing out it's going up. A map uses colors to show which places use a lot of renewable energy, making those spots stand out. A bar chart compares renewable energy sources' contributions, highlighting solar or wind dominance. An interactive dashboard integrates these visuals, allowing policymakers to comprehend and compare sustainability efforts across regions. This visualization aids in advocating for sustainable energy policies, facilitating informed decisions for a positive social and environmental impact.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- An Phan Data Engineer | AI Solutions Engineer at AITOMATIC, Inc.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Translate complex data into visual representations for clear understanding, empowering stakeholders to grasp sustainability and social impact insights effortlessly.
LikeLike
Celebrate
Support
Love
Insightful
Funny
Load more contributions
5 Data ethics
Data ethics is the study and practice of the moral and social implications of data mining and data engineering. It can help you ensure that your data mining techniques are fair, responsible, and beneficial for sustainability and social impact, as well as respect the rights and interests of your data sources and stakeholders. Moreover, data ethics can address various issues and challenges in data mining and data engineering, such as data quality, privacy, security, governance, and accountability. Data quality involves making sure your data is accurate, reliable, and relevant for your purpose. Data privacy requires protecting the confidentiality, anonymity, and consent of your data sources. Data security entails preventing unauthorized access or use of your data. Data governance involves establishing rules, policies, and standards for your data collection. Lastly, data accountability means monitoring, evaluating, and reporting the outcomes of your data mining techniques.
Help others by sharing more (125 characters min.)
- Jerome Jayanathan Needhipathi Delivery Lead @ Shell | Doctorate in Business Administration - specializing in AI Governance.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Implementing ethical data practices for sustainability involves steps like policy creation, establishing governance structures, ensuring data quality, protecting privacy, enhancing security, ensuring transparency, training staff, and continual improvement. These efforts aim to align data usage with ethical standards, safeguard privacy, maintain data accuracy, and foster a culture of responsibility. By integrating these steps, organizations can ensure ethical data handling for sustainable endeavors and positive societal impact.
LikeLike
Celebrate
Support
Love
Insightful
Funny
1
- Temitope Adeyeha Data Engineering | ML Research
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
As we harness the power of data for sustainability and social impact, ethical considerations become the cornerstone of responsible data mining. Ethical data practices encompass transparency, fairness, and privacy. It's not just about what data can reveal, but how it should be handled and utilized ethically. Ensuring the responsible collection and use of data is paramount, especially when dealing with sensitive social and environmental issues. Striking the right balance between extracting valuable insights and safeguarding individual rights is not only a legal obligation but a moral imperative. Upholding ethical standards in data mining amplifies the positive impact on society and ensures a sustainable, equitable future.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- Santhosh Nalgonda IT Software Engineering Specialist at BCG l 6k Plus Followersl Ex ZS I Backend Developer Enabling GenAI l AWS Certified Architect- Analytics
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Data ethics is a critical aspect of responsible and ethical data management, encompassing the ethical considerations and guidelines associated with the collection, storage, processing, and sharing of data. It involves ensuring that data practices align with principles of privacy, fairness, transparency, and accountability. Ethical data handling requires obtaining informed consent for data collection, protecting individuals' privacy rights, and preventing discrimination in data-driven decision-making processes. Transparency in data practices involves clearly communicating how data is used and providing individuals with control over their data.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- An Phan Data Engineer | AI Solutions Engineer at AITOMATIC, Inc.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Uphold ethical standards in data collection, analysis, and usage, safeguarding privacy, mitigating biases, and ensuring responsible practices in sensitive areas.
LikeLike
Celebrate
Support
Love
Insightful
Funny
6 Data mining techniques for sustainability and social impact
Data mining is a powerful and versatile tool for data engineering that can help you solve various problems and challenges in sustainability and social impact. However, data mining also requires careful planning, execution, and evaluation to ensure its effectiveness and ethicality. By applying the data mining techniques discussed in this article, you can improve your data quality, discover hidden patterns, classify and cluster your data, visualize and communicate your data, and respect and protect your data ethics. These data mining techniques can help you achieve your sustainability and social impact goals and make a positive difference in the world.
Help others by sharing more (125 characters min.)
- Mudra P. MLOps Engineer | Harvard WECode24 | Technical Writer
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Use data mining results to identify areas with high waste production. This could be specific neighborhoods, industrial zones, or commercial areas. Understanding these hotspots is crucial for targeted waste management strategies. Classify and characterize the types of waste generated in different areas. This information is valuable for designing recycling programs, waste separation initiatives, and sustainable disposal methods.Analyze the data to identify potential recycling opportunities. This may involve understanding the types of materials commonly discarded and assessing the feasibility of recycling programs for those materials.
LikeLike
Celebrate
Support
Love
Insightful
Funny
- An Phan Data Engineer | AI Solutions Engineer at AITOMATIC, Inc.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Employ sentiment analysis and anomaly detection methods tailored to sustainability goals, offering insights into public sentiments and identifying critical irregularities for intervention.
LikeLike
Celebrate
Support
Love
Insightful
Funny
7 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
Help others by sharing more (125 characters min.)
- Vijay S.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Text Mining and Natural Language Processing (NLP): Text mining and NLP can extract valuable information from textual data such as social media posts, news articles, and reports. These techniques can gauge public sentiment on social and environmental issues, track the spread of information or misinformation, and identify emerging sustainability trends.
LikeLike
Celebrate
Support
Love
Insightful
Funny
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Generative AI can support policymakers in developing evidence-based policies and regulations to address sustainability challenges. By analyzing large datasets and generating insights, AI algorithms can inform decision-making processes and identify effective strategies for achieving social and environmental goals.
LikeLike
Celebrate
Support
Love
Insightful
Funny
Data Engineering
Data Engineering
+ Follow
Rate this article
We created this article with the help of AI. What do you think of it?
It’s great It’s not so great
Thanks for your feedback
Your feedback is private. Like or react to bring the conversation to your network.
Tell us more
Tell us why you didn’t like this article.
If you think something in this article goes against our Professional Community Policies, please let us know.
We appreciate you letting us know. Though we’re unable to respond directly, your feedback helps us improve this experience for everyone.
If you think this goes against our Professional Community Policies, please let us know.
More articles on Data Engineering
No more previous content
- You're knee-deep in optimizing ETL pipelines. How do you prevent burnout and stay motivated? 5 contributions
- Your data processing pipeline faces sudden schema changes. How will you adjust to ensure seamless operation? 12 contributions
- Your client doubts the accuracy of your data outputs. How can you address their concerns effectively? 10 contributions
- Your team is resistant to change. How can you convince them of the importance of real-time data processing? 6 contributions
- You're leading a data infrastructure initiative. How do you ensure all team members align on project goals? 3 contributions
- Your data processing pipeline faces sudden schema changes. How will you adjust to ensure seamless data flow? 2 contributions
- Your team is facing conflicting timelines. How can you align data scientists and data engineers for success? 3 contributions
- You're tasked with enhancing data processing performance. How do you ensure accuracy remains uncompromised? 3 contributions
No more next content
Explore Other Skills
- Programming
- Web Development
- Agile Methodologies
- Machine Learning
- Software Development
- Computer Science
- Data Analytics
- Data Science
- Artificial Intelligence (AI)
- Cloud Computing
More relevant reading
- Data Mining You're facing data bias challenges in data mining. How can you ensure the accuracy of your results?
- Data Mining Dealing with missing data in data mining analysis. How do you prevent biases from creeping in?
- Data Mining What role does data preprocessing play in enhancing data mining results?
- Data Mining What are some techniques for staying resilient when data mining is difficult?