Build Flexibility Into Data Mining Programs

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Best Practices in Data Mining" The speaker is Mark Flaherty, CMO at InetSoft.

So you would want to have a system in place where you can deploy new models seamlessly in the backend that impact your customers or your CSRs or whatever interact with those models in real time. And those systems are fairly convenient to put together with today’s technology so that you can actually update your response rate immediately. So if you see changes in the market, and you have done the data analysis to see how those changes in the market can impact your models and impact the patterns that the models are using, you can actually apply those in real-time while people are interactively using your systems.

Moderator: We know things are going to happen to our input streams, that there are going to be missing elements. There are going to be outliers. There is going to be data coming in from third-parties, Twitter streams, maybe you are buying data from data compilers. They may change their interfaces. They may change what the data elements mean. So it’s important for us to be looking for problems to happen. There are two best practices for dealing with this uncertainty. One is to document the models enough to know what’s happening to be able to diagnose a problem if you see a data element starting to drop out that should be there. Put that documentation in a repository with the rest of the code for that application. The second is one has to do with handling missing data and handling outliers.

Flaherty: Yes, clean the data. Fix it.

Moderator: Yes, that’s one of these best practices everywhere. If you get down to performance issues that’s when you want to hard code things, for example, but anytime you can employ a useable and reasonable layer of abstraction around managing large data sets, that’s preferred.

Flaherty: If we think about the time dimension, one of the best practices is that in order to run these models frequently and in many more kind of situations, you don’t need that complex a model. You just need to build simpler models which can provide a greater degree of flexibility versus a complex model which might prove to be difficult to be used in a constantly changing kind of an environment. So that’s one thing. And you want to hold on to those old models and descriptions of those old models. That’s where the governance comes in as definitely you want to have lot of transparency across the different stakeholders who are involved in the modeling workflow, and so documentation is as critical there from a compliance kind of perspective and governance kind of perspective.

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How Is Data Mining Related to AI-Based Data Analysis?

Data mining and AI-based data analysis are closely related concepts within the realm of data science, each contributing to the process of extracting valuable insights from large datasets. Here's how they are interconnected and complement each other:

1. Definitions and Core Concepts:

• Data Mining:
  • Data mining is the process of discovering patterns, correlations, anomalies, and other significant structures in large datasets using statistical and computational techniques. It involves methods like clustering, classification, regression, association rule mining, and anomaly detection.
• AI-Based Data Analysis:
  • AI-based data analysis involves using artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning, to analyze and interpret data. These techniques enable systems to learn from data, make predictions, and automate decision-making processes.

2. Complementary Roles:

• Data Mining Techniques:
  • Data mining employs a variety of algorithms and statistical models to identify patterns and relationships in data. Traditional data mining methods include decision trees, k-means clustering, and association rule mining.
• AI Algorithms:
  • AI-based data analysis uses advanced algorithms, including neural networks, support vector machines, and ensemble methods. These AI algorithms can handle complex and non-linear patterns in data, often with greater accuracy and adaptability than traditional methods.

3. Integration of AI in Data Mining:

• Enhanced Pattern Recognition:
  • AI enhances data mining by improving pattern recognition capabilities. For example, deep learning algorithms can automatically identify intricate patterns and features in unstructured data, such as images, text, and audio, which traditional data mining methods might miss.
• Automation and Efficiency:
  • AI automates the data mining process by enabling systems to learn from data and adapt to new information without human intervention. This reduces the time and effort required for manual analysis and increases efficiency.
• Scalability:
  • AI-based data analysis scales more effectively with large and complex datasets. Machine learning models can be trained on vast amounts of data, making them suitable for big data environments where traditional data mining techniques might struggle.

4. Applications and Use Cases:

• Predictive Analytics:
  • Both data mining and AI are used in predictive analytics to forecast future trends based on historical data. AI-based models, however, often provide more accurate and nuanced predictions due to their ability to capture complex relationships.
• Customer Segmentation:
  • Data mining techniques like clustering are used to segment customers into distinct groups. AI enhances this process by dynamically updating segments based on new data and uncovering deeper insights into customer behavior.
• Fraud Detection:
  • In fraud detection, data mining identifies suspicious patterns and anomalies. AI algorithms can improve detection rates by learning from historical fraud patterns and adapting to new types of fraud.
• Healthcare Analytics:
  • Data mining in healthcare involves identifying correlations and trends in patient data. AI-based analysis goes further by predicting disease outbreaks, personalizing treatment plans, and improving diagnostic accuracy.

5. Challenges and Considerations:

• Data Quality and Preprocessing:
  • Both data mining and AI require high-quality data. Data preprocessing, such as cleaning, normalization, and transformation, is crucial for accurate analysis.
• Model Interpretability:
  • While AI models, particularly deep learning, can be highly accurate, they are often seen as "black boxes" with limited interpretability. Data mining techniques tend to be more transparent, allowing for easier understanding and validation of results.
• Computational Resources:
  • AI-based data analysis typically demands more computational power and resources compared to traditional data mining. Ensuring adequate infrastructure is essential for leveraging AI effectively.