Beginning with the first stage, data organization, one of the first steps is typically data parsing, determining the structure of the data so that it can be imported into a data analysis software environment or package. Another common step is data integration, which aims to acquire, consolidate and restructure the data, which may exist in heterogeneous sources (for example, flat files, XML, JSON, relational databases), and in different locations. It may also require the alignment of data at different spatial resolutions or on different timescales. Sometimes the raw data may be available in unstructured or semi-structured form. In this case it is necessary to carry out information extraction to put the relevant pieces of information into tabular form. For example, natural language processing can be used for information extraction tasks from text (for example, identifying names of people or places). Ideally, a dataset should be described by a data dictionary or metadata repository, which specifies information such as the meaning and type of each attribute in a table. However, this is often missing or out-of-date, and it is necessary to infer such information from the data itself. For the data type of an attribute, this may be at the syntactic level (for example, the attribute is an integer or a calendar date), or at a semantic level (for example, the strings are all countries and can be linked to a knowledge base, such as DBPedia).6
In the second stage of data engineering, data quality, a common task is standardization, involving processes that convert entities that have more than one possible representation into a standard format. These might be phone numbers with formats like "(425)-706-7709" or "416 123 4567," or text, for example, "U.K." and "United Kingdom." In the latter case, standardization would need to make use of ontologies that contain information about abbreviations. Missing data entries may be denoted as "NULL" or "N/A," but could also be indicated by other strings, such as "?" or "-99." This gives rise to two problems: the identification of missing values and handling them downstream in the analysis. Similar issues of identification and repair arise if the data is corrupted by anomalies or outliers. Because much can be done by looking at the distribution of the data only, many data science tools include (semi-)automated algorithms for data imputation and outlier detection, which would fall under the mechanization or assistance forms of automation.
Data Mining for the Masses, Second Edition: with implementations in RapidMiner and R free 558
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The goal of EDA was described as hypothesis generation, and was contrasted with confirmatory analysis methods, such as hypothesis testing, which would follow in a second step. Since the early days of EDA in the 1970s, the array of methods for data exploration, the size and complexity of data, and the available memory and computing power have all vastly increased. While this has created unprecedented new potential, it comes at the price of greater complexity, thus creating a need for automation to assist the human analyst in this process.
At the same time, because of the complex dependencies between hyperparameters, sophisticated methods are needed for this optimization task. Human experts not only face the problem of determining performance-optimizing hyperparameter settings, but the choice of the class of machine learning models to be used in the first place, and the algorithm used to train these. In automated machine learning (AutoML) all these tasks, often along with feature selection, ensembling and other operations closely related to model induction, are fully automated, such that performance is optimized for a given use case, for example, in terms of the prediction accuracy achieved based on given training data.
Data mining programs help with extracting knowledge from large amounts of data. Despite decades of experience with profile testing, laboratory medicine is just now realizing the practical application of these programs to highly parallel analytical techniques (genomics, proteomics, etc.). Professional data preparation, and most specifically data normalization is crucial for the success of any data mining project. Using routine hospital admission data, we demonstrate how explorative cluster analysis can identify meaningful result patterns. Based upon this feasibility study, the German Association for Clinical Chemistry and Laboratory Medicine is now supporting a research and software development project.
In this study, a novel two-layer classification framework was developed, as the SVM model in the first-layer classifier was trained with all the training datasets (oogenesis, spermatogenesis and embryogenesis), serving to predict a query protein sequence as fertility or non-fertility related protein. The SVM models in the second layer were trained with oogenesis, spermatogenesis and embryogenesis training datasets, separately as binary predictor to further identify the class of the predicted protein in the previous layer (oogenesis, spermatogenesis or embryogenesis).
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