# Occam's Razor for _Big Data_? On Detecting Quality in Large Unstructured Datasets
## 3 Big Data Analytics: From Machine Learning to Artificial Intelligence
### Clustering Algorithms: Old Dogs for New Tricks  
Clustering has been around for a rather long time [25] and, as has become clear from the overview given in the previous section, before diving into the deep blue sea of deep learning, clustering methods are among the most essential parsimonious analytics to consider first. They prove ideal candidates for building on simplicity in order to (1) improve the quality of large data analysis and (2) minimize the necessary computation times. Cluster analysis is well-suited for identifying meaningful groups in large and unknown datasets [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. Algorithms such as \"k-means\" [37] use pair-wise similarity metrics such as the Euclidean to identify the cluster-vertex memberships, where the Euclidean is by far the most popular choice for \"k-means\" functions. Considerable reduction in computation times can be obtained through optimization of similarity metrics [47] and local heuristics [48]. For cluster algorithms to provide accurate results, characteristics in the training data need to be captured by the algorithm. Cluster algorithms are dedicated to specific data topologies, hence the need for data normalization. Yet, the smoothing of data results in a loss of information [49; 50; 51; 52; 53]. For a similarity metric to improve prediction quality, the similarity metric needs to reflect the core attributes of each dataset [48], a requirement that is satisfied the \"hpLysis\" software solution for example [53]. The use of data normalization strategies may hide key features of datasets [49], an observation that may explain, just to give an example, why genes with a strong differentiation are not always detected in microarray analysis [49]. The default strategy here consists of applying averaged normalization [53; 54; 55; 56; 57; 58]. While [59] argues for tuning algorithms towards data topologies, this strategy is rarely applied. There is no \"best clustering algorithm\", and any algorithm imposes a structure on the data given [59]. Figure 3: Functional architecture of a deep neural network (DNN) (top) with four fully connected hidden layers of neurons between input and output layers, and that of a self-organized map (SOM) with a single layer of fully connected neurons between input and output. The SOM reduces the input dimensionality in order to represent the input distribution as a map. For learning, it generally uses winner-take-all, while a DNN may use a multitude of learning algorithms, at multiple steps of processing. To address issues relative to data topologies, [59; 60; 61; 62; 63], strategies for dimensionality reduction and feature extraction as a pre-processing step to clustering are proposed [61]. However, these strategies are known to provide a relatively poor performance [64] when compared with the fine-tuning of algorithmic building blocks [45]. Clustering algorithms are generally used for image segmentation, tracking of individuals in CCTVs, classification of digital signals, and so forth. Comparisons of measurements from 20+ cluster algorithms and 300+ correlation metrics have shown that established cluster algorithms have a speed and accuracy considerably higher than that of other recently proposed algorithms. Some work [49; 50] identified 100+ new cluster algorithms that may be successfully applied to large datasets in fields such as digital signal processing and/or image analysis. Measurements across a large number of cluster algorithms and more than 100 real-life datasets reveal how the right parameter choice for clustering can outperform a large number of other recent algorithmic approaches to the problem of large datasets [65]. In the case of big data, the motivation is to increase the performance of clustering for data mining, and to identify new data mining algorithms without \"re-inventing the wheel\" altogether. This can be achieved, as argued here, by the integration of \"trivial\" algorithms to outperform more \"complex\" ones [48; 49].