DATA MINING

 DATA MINING KDD PROCESS

KDD Knowledge Delivery in Data Base

            KDD, or Knowledge Discovery in Databases, is a comprehensive process aimed at uncovering valuable knowledge from data. It highlights the application of various data mining techniques to extract insights. This interdisciplinary field attracts researchers from diverse areas such as artificial intelligence, machine learning, pattern recognition, databases, statistics, expert systems, and data visualization.

            The primary goal of the KDD process is to extract valuable information from large datasets. This is achieved through the application of data mining algorithms, which help identify and define what constitutes useful knowledge.

             Knowledge Discovery in Databases (KDD) involves a systematic and exploratory approach to analyzing and modeling large data repositories. It is an organized process aimed at identifying valid, useful, and understandable patterns within vast and complex datasets. Central to KDD is Data Mining, which encompasses the application of algorithms to explore the data, build models, and uncover previously unknown patterns. These models are then used to extract insights, analyze the data, and make predictions.

             The vast availability and abundance of data today make knowledge discovery and data mining increasingly crucial and significant. Given the recent advancements in the field, it is no surprise that specialists and experts now have access to a diverse array of techniques.

THE KDD PROCESS

               The knowledge discovery process, as illustrated in the provided figure, is both iterative and interactive, encompassing nine distinct steps. The iterative nature of the process means that revisiting previous stages may be necessary. Given its complex and creative aspects, there is no single formula or comprehensive scientific classification for making the correct decisions at each step or for every type of application. Therefore, a thorough understanding of the process, along with the specific requirements and possibilities at each stage, is essential.

                The knowledge discovery process, as depicted in the provided figure, is characterized by its iterative and interactive nature, consisting of nine distinct steps. This iterative process often requires revisiting earlier stages. Due to its complexity and creative demands, there is no one-size-fits-all formula or universal scientific classification for making the right decisions at each step or for every application type. Therefore, it is crucial to have a deep understanding of the process and to recognize the specific requirements and opportunities at each stage.

        

        

1. Building up an understanding of the application domain:

             This is the initial preliminary step, setting the stage for determining the necessary actions such as data transformation, algorithm selection, and representation. Those overseeing a KDD project must understand and define the end-user’s objectives and the context in which the knowledge discovery process will take place, including relevant prior knowledge

2. Choosing and creating a data set on which discovery will be performed:

               Once the objectives are defined, the next step is to determine the data that will be used in the knowledge discovery process. This involves identifying the available data, acquiring relevant data, and integrating it into a cohesive dataset for analysis. This integration is crucial because Data Mining relies on the available data to learn and discover insights. The quality of the models built depends on the completeness of the data; missing significant attributes can undermine the entire study. However, managing and processing extensive data repositories can be costly and complex. The process involves an iterative and interactive approach, starting with the best available datasets and gradually expanding them while assessing their impact on knowledge discovery and modeling.

3. Preprocessing and cleansing:

              In this step, the focus is on enhancing data reliability through data cleansing. This involves addressing issues such as missing values and removing noise or outliers. Techniques may include complex statistical methods or data mining algorithms. For instance, if a specific attribute is suspected of being unreliable or has substantial missing data, it can become the target for a supervised data mining algorithm. A prediction model can be developed for this attribute to estimate and fill in the missing values. The extent of attention given to this stage depends on various factors, but thoroughly examining these aspects is crucial and often reveals valuable insights into enterprise data systems.

4. Data Transformation:

                 In this stage, the data is prepared and developed for Data Mining. This involves techniques such as dimension reduction (e.g., feature selection and extraction) and record sampling, as well as attribute transformation (e.g., discretizing numerical attributes and applying functional transformations). This step is critical to the success of the KDD project and is often tailored to the specific needs of the project. For instance, in medical assessments, the combination of attributes may be more important than any single attribute on its own. In business contexts, factors beyond our control, such as the effects of advertising campaigns, may need to be considered. If the initial transformations are not appropriate, they may lead to unexpected results, necessitating adjustments in subsequent iterations. Thus, the KDD process is iterative, with each stage informing the next and refining the understanding of necessary transformations.

5. Prediction and description 

                   At this stage, we decide which type of Data Mining to employ, such as classification, regression, or clustering. This choice is primarily guided by the objectives of the KDD project and the outcomes of the previous steps. Data Mining generally serves two main purposes: prediction and description. Prediction, which falls under supervised Data Mining, involves making forecasts based on historical data. In contrast, descriptive Data Mining, which encompasses unsupervised learning and data visualization, focuses on uncovering patterns and insights without prior labels. Most Data Mining techniques rely on inductive learning, where a model is developed by generalizing from a sufficient number of training examples. The underlying assumption is that this model will be applicable to future cases. Additionally, the process considers meta-learning, which evaluates how well different techniques perform on the specific dataset at hand.

6. Selecting the Data Mining algorithm:

                     With the technique selected, the next step is to determine the strategies for implementation. This involves choosing a specific approach for pattern discovery, which may include various inducers. For instance, if precision is the priority, neural networks might be preferable, whereas if understandability is more important, decision trees could be a better choice. Meta-learning plays a crucial role in this stage by analyzing what makes a Data Mining algorithm successful for a given problem. It seeks to understand the conditions under which a particular algorithm performs best. Each algorithm has its own parameters and learning strategies, such as ten-fold cross-validation or other methods for dividing data into training and testing sets.

7. Utilizing the Data Mining algorithm:

                    Finally, the implementation of the Data Mining algorithm takes place. This stage often requires running the algorithm multiple times to achieve satisfactory results. For example, adjustments may be made to the algorithm's control parameters, such as the minimum number of instances allowed in a single leaf of a decision tree, until the desired outcome is achieved.

8. Evaluation:

                       In this step, we evaluate and interpret the mined patterns and rules to ensure they align with the objectives defined in the initial stage. This involves examining the impact of preprocessing steps on the results of the Data Mining algorithm. For instance, if a feature added in step 4 affects the outcome, we may need to revisit and refine earlier steps. The focus here is on the comprehensibility and utility of the resulting model. Addition/ally, the discovered knowledge is documented for future reference. The final step involves applying the findings, gathering overall feedback, and assessing the results obtained from the Data Mining process.

9. Using the discovered knowledge:

                      At this point, we are ready to integrate the acquired knowledge into another system for practical application. This integration allows us to make adjustments to the system and evaluate the resulting impacts. The success of this step determines the overall effectiveness of the KDD process. However, several challenges may arise, such as the loss of the "laboratory conditions" under which the data was initially analyzed. For example, knowledge was derived from a static dataset, but once integrated, the data becomes dynamic. Data structures may evolve, some quantities may become unavailable, and the data domain might change, such as the emergence of unexpected attribute values.