The process of discovering knowledge and data from various sources is referred to as Knowledge Discovery in Databases (KDD). It involves applying data mining techniques in a high-level manner to extract meaningful insights. KDD is a subject of interest for researchers in fields such as machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, and data visualization.
The main objective of the KDD process is to derive knowledge from data in the context of large databases. This process involves nine steps, which are interactive and repetitive. It is important to note that one may have to return to previous steps during the process. The KDD process involves many artistic aspects, which means that no one formula or technique can be used to make the right choices for each step and application type. Therefore, it is essential to grasp the process and understand the different requirements and possibilities for each step.
The KDD process involves nine steps that begin with setting goals and end with implementing the discovered knowledge. The loop is closed by making changes in the application domain, followed by measuring the effects on new data repositories. This leads to the start of the KDD process again.
Hey there! If you’re curious about the KDD process, I’ve got you covered. Let me walk you through the nine stages involved in this exciting journey towards discovering hidden insights and valuable knowledge. Get ready to learn something new!
Stage 1
To begin a Knowledge Discovery in Databases (KDD) project, it is important to first develop an understanding of the application domain. This preparatory step involves defining the goals of the end user, identifying where the knowledge discovery process will take place, and considering any relevant prior knowledge. This will set the scene for determining the appropriate transformation, algorithms, and representation to be used in the project.
During the KDD process, pre-processing of data begins in the next three steps, which may be revised as needed. Some methods used in this step may be similar to data mining algorithms.
Step 2
Data is crucial in knowledge discovery, as it helps identify what information is available, obtain additional necessary data, and integrate everything into one dataset, including the attributes that will be considered for the process. This is important because data mining discovers and learns from the available data, which serves as the evidence base for constructing models. If some crucial attributes are missing, the entire study may fail. Therefore, it’s better to consider more attributes.
However, collecting, organizing, and operating complex data repositories can be expensive, and there is a trade-off between the cost and the opportunity for a better understanding of the phenomenon. This trade-off is an aspect where the interactive and iterative nature of KDD takes place. It starts with the best available dataset and later expands and observes the effect in terms of knowledge discovery and modeling. Three primary sources of data include a data warehouse, one or more transactional data, or one or more flat tables.
Stage 3
During the pre-processing and cleansing stage, data is cleared and enhanced to improve its reliability. This includes handling missing values and removing outliers. Complex statistical methods or data mining algorithms may be used for this purpose. For instance, if a certain attribute is suspected to be unreliable or has many missing data, it can be the target of a data mining supervised algorithm. A prediction model can be developed for this attribute, and the missing data can be predicted. The level of attention paid to this stage depends on various factors. However, studying these aspects is crucial and can often reveal important insights about enterprise information systems.
Stage 4
Data transformation is the next step in KDD. It involves preparing better data for mining through methods such as feature selection, record sampling, and attribute transformation. This step is crucial for project success and is tailored to fit each project. The KDD process self-reflects and leads to an understanding of the transformation required. The next four steps relate to data mining and focus on algorithmic aspects for each project.
Stage 5
When it comes to data mining, it’s important to choose the appropriate task based on the KDD goals and previous steps. There are two major goals in data mining: prediction and description. Prediction, also known as supervised data mining, involves making predictions based on existing data, whereas descriptive data mining includes unsupervised techniques and visualization aspects.
Most data mining techniques rely on inductive learning, where a model is created by generalizing from a sufficient number of training examples. The assumption is that the trained model can be applied to future cases. This approach also considers the level of meta learning for the available data set.
Stage 6
When it comes to data mining, choosing the right algorithm is essential. Once you have a strategy in place, you can determine the methods for searching patterns. There are various options available, including using multiple inducers. For instance, if you prioritize precision, neural networks are a better choice, while decision trees are more suitable if you prioritize understandability.
Meta learning involves exploring the causes behind a data mining algorithm’s success or failure in a given problem. This approach aims to identify the circumstances under which a data mining algorithm is most effective. Each algorithm has its parameters, along with learning tactics such as tenfold cross-validation or other methods of dividing data for training and testing purposes.
Stage 7
The next step is to utilize a data mining algorithm. Once you have chosen the appropriate algorithm, you can implement it. It may be necessary to run the algorithm multiple times until a satisfactory result is obtained. For example, you may need to adjust the algorithm’s control parameters, such as the minimum number of instances in a single decision tree leaf.
Stage 8
During the evaluation stage of data mining, we analyze and interpret the patterns that have been found in relation to the goals we set in the first stage. This involves assessing how the pre-processing steps have affected the results of the data mining algorithm. The main focus of this stage is to ensure that the model is easy to understand and useful. Additionally, any knowledge that has been discovered is documented for future reference.
Finally, the last step involves using the patterns that have been discovered and providing feedback on the results of the data mining process.
Stage 9
Now that we have discovered knowledge, it’s time to incorporate it into another system to take further action. The knowledge becomes active as we can make changes to the system and measure the effects. The success of this step is crucial as it determines the effectiveness of the entire KDD process.
However, there are several challenges we may face in this step. One of the challenges is losing the laboratory conditions under which we operated while discovering the knowledge. For example, the knowledge was discovered from a certain static snapshot, which is usually a sample of the data. But now, the data becomes dynamic, and its structures may change, and the data domain may be modified.
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