The machine learning algorithm t-Distributed Stochastic Neighborhood Embedding, also abbreviated as t-SNE, can be used to visualize high-dimensional datasets. Each high-dimensional information of a data point is reduced to a low-dimensional representation. However, the information about existing neighborhoods should be preserved.
So this technique is another tool you can use to create meaningful groups in unordered data collections based on the unifying data properties. If you don’t know what cluster algorithms are, check out this article. Here we present 5 machine learning methods that you should know. As shown in the following figure, the data should be represented grouped in 2-dimensional space.
But how does the algorithm work and what are its strengths? In order to understand its function, we need to look at the origin of the technology.
What is the Stochastic Neighbor Embedding (SNE) Algorithm?
The basis of the t-Distributed Stochastic Neighborhood Embedding algorithm is originally the Stochastic Neighbor Embedding (SNE) algorithm. This converts high-dimensional Euclidean distances into similarity probabilities between individual data points. The probability with which an object occurs next to a potential neighbor must be calculated. The dissimilarities between two high-dimensional data points can be explained with a distance matrix, corresponding to the squared Euclidean distance. A conditional probability is calculated for the low-dimensional correspondence. This determines the similarity of the two data points on the low-dimensional map.
In order to achieve the closest possible correspondence between the two distributions pij and qij, a Kullback-Leibler divergence (KL) over all neighbors of each data point is computed as a cost function C. Large costs are incurred for distant data points.
A gradient descent method is used to optimize the cost function. However, this optimization method converges very slowly. In addition, a so-called crowding problem arises.
If a high dimensional data set is linearly approximated in a small scale, then it cannot be reduced to a lower dimension with a local scaling algo- rithm to a lower dimension.
What makes the t-Distributed Stochastic Neighborhood Embedding (t-SNE) Algorithmt work?
The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm starts here. On the one hand, a simplified symmetric cost function is used.
Here, only one KL is minimized over a common probability distribution of all high, and low dimensional data is minimized.
On the other hand, the similarity of the low-dimensional data points is computed with a Student’s t-distribution and a degree of freedom of one. This can be optimized quickly and is stable to the crowding problem. stable against the crowding problem.
It’s almost harder to understand all the acronyms around Artificial Intelligence (AI) than the technology itself. AI vs Machine Learning vs Deep Learning – These terms are often carelessly mixed together. But what are actually the differences? In this article, we will introduce you to all Three fields, because even though there is overlap, they differ. It should be important for you to know these differences, as each discipline describes different stages of a data analysis pipeline.
AI vs Machine Learning vs Deep Learning
In the following figure, we have schematically shown you the individual fields in their context. As you can see, the individual disciplines surround each other and form an onion-like layered model.
The figure clearly shows that there are relationships between individual disciplines. AI is to be understood as a generic term and thus includes the other fields. The deeper you go in the model, the more specific the tasks become. In the following, we will follow this representation and work our way from the outside to the inside.
All disciplines are encompassed by the term AI. It is a science that explores ways to build intelligent programs and machines that can perceive, reason, act, and solve problems creatively. To this end, it attempts to model how the human brain works. The following figure shows that AI can basically be divided into two categories.
Classification is about measuring the performance of AI based on how well it is able to replicate the human-like brain. In the Based on Functionality category, AI is classified based on how well it matches the human way of thinking. In the second category, it is evaluated based on human intelligence. Within these categories, there are still some subgroups that correspond to an index.
AI vs Machine Learning
So what is the first subcategory Machine Learning and how does it differ from AI? While AI deals with the functioning of artificial intelligence and compares them with the functioning of the human brain, machine learning is a collection of mathematical methods of pattern recognition. It is about how a system is given the ability to automatically learn and improve from experience. Various algorithms (e.g., neural networks) are used for this purpose. In the following scheme, the broad machine learning field is presented in a categorized way.
In machine learning, algorithms are used to build statistical models based on training data. Roughly, these algorithms can be divided into three main learning techniques. While in supervised learning the result is predetermined by a cleanly labeled data set, unsupervised learning is completely self-organized. Here the patterns are to be explored independently. In reinforcement learning, utility functions are to be independently approximated based on rewards received.
Machine Learning vs Deep Learning/ Deep Neural Learning
Deep learning is a subfield of machine learning similar to machine learning in Ai. Here, multilayer neural networks are used to analyze various factors in large amounts of data. These networks are similar to the human neural system. If you want to know more about this structure, read our article on perceptrons, the smallest unit of a neural network. Optimization of neural weights, unlike machine learning, can be done using powerful GPUs. Pure machine learning is best used on structured data sets, while for unstructured data you should opt for deep learning. In the following graphic, we have summarized the main factors that make up deep learning. For the network types autoencoder and CNN we provide more detailed articles.
There is a lot of Big Data software available now. One of them that you should definitely know about is the H2O AI Machine Learning solution.
With this open-source application you can implement algorithms from the fields of statistics, data mining and machine learning. The H2O AI Engine is based on the distributed file system Hadoop and is therefore more performant than other analysis tools. Your machine learning methods can thus be used as parallelized methods.
They can program their algorithms in R, Python and Java and thus in the most important mathematical programming languages. H2O provides a REST interface to Python, R, JSON and Excel. Additionally, you can access H2O directly with Hadoop and Apache Spark. This makes integration into your data science workflow much easier. You already get approximate results while running the algorithms. A graphical web browser UI helps you to better analyze the processes and perform targeted optimizations.
How Clients Interacts with H2O AI
You can interact with H2O via clients using various interfaces. It is important for you to know that the data is usually not held in memory. They are localized in a H2O cluster and you only get a pointer to the data when you make a request.
The basic unit of data storage accessible to you is the H2O Frame. This corresponds to a two-dimensional, resizable and potentially heterogeneous data point. This tabular data structure also contains labeled axes.
Your H2O cluster consists of one or more nodes. A node corresponds to a JVM process and this process consists of three layers.
H2O Machine Learning Components
The R evaluation layer is a slave to the REST client front-end and in the Scala layer you can write native programs and algorithms. You can then use these with H2O Machine learning.
This layer is where your algorithms are applied. You can run statistical methods, data import and machine learning here.
In this layer you handle the resource management. You can manage both the memory and the CPU processing capacity.
As a data scientist, you have several basic tools at your disposal, which you can also apply in combination to a data set. Here we present some clustering algorithms that you should definitely know and use
In times of Big Data, not only the sheer number of data increases, but also the relationships between them. More and more complex dependencies are formed. This makes it all the more difficult to recognize these similar properties and to assign the data to so-called clusters in a way that can be evaluated.
You have certainly heard of these algorithms and maybe used one or the other, but do you really know what clustering algorithms are?
What are clustering algorithms?
So let’s first clarify what these algorithms are in the first place. The goal is clear: You want to identify similar properties between individual data points in a data set and group them in a meaningful way. These properties are often high-dimensional.
With the help of cluster analysis, you want to reduce this high-dimensional information to a low-dimensional dependency. So, for example, a representation in 2D space. Clustering is an unsupervised machine learning technique and in the end you classify the data points by using algorithms.
The approach to clustering differs from technique to technique. All have their advantages and disadvantages, so it makes sense to try several on one set of data, or apply them in combination. Below we will introduce you to some popular clustering methods and explain their grouping approach.
The first algorithm we want to introduce you to is Mean-Shift Clustering. With this you can find dense areas of data points according to the concept of kernel density estimation (KDE). The basis of the clustering is a circular sliding window, which moves towards higher density at each iteration. Within the window, the centers of each class are determined, called centroids.
The movement is now created by moving the center to the average of the points within the window. The density within the sliding window is thus proportional to the number of points within it. This motion continues until there is no direction in which the motion can take more points within the kernel.
Hierarchical Cluster Analysis (HCA)
With HCA, clusters are formed based on empirical similarity measures of the data points. This means that the two most similar objects are assigned one after the other until all objects are in one cluster. This results in a tree-like structure. In contrast to the K-means algorithm, which we will discuss later, similarities between the clusters play a role. These are represented by a cluster distance. With K-means, only all objects within a collection are similar to each other, while they are dissimilar to objects in other clusters.
You can create an HCA in different ways. There are two elementary procedures, the top-down and the bottom-up. If you want to know more about Hierarchical Cluster Analysis, read this article.
Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
GMM basically assumes that the data points are Gaussian and not circular. The clusters are described by their mean and standard deviation. Each Gaussian distribution is randomly assigned to a single cluster and found using the Expectation-Maximization (EM) optimization algorithm. The probability of belonging to a cluster is then calculated for each data point. Thus, the closer a point is to the Gaussian center, the more likely it is then to belong to that cluster. Based on these probabilities, a new set of parameters for the Gaussian distributions is iteratively calculated. That is, the probabilities within a cluster are maximized.
K-Means clustering algorithms
The k-Means algorithm described by MacQueen, 1967 goes back to the methods described by Lloyd, 1957 and Forgy, 1965. You can use the algorithm besides cluster analysis also for vector quantization. Here, a data set is partitioned into k groups with equal variance.
The number of clusters must be specified in advance. Each disjoint cluster is described by the average of all contained samples. The so-called cluster centroid.
Each centroid is updated to represent the average of its constituent instances. This is done until the assignment of instances to the clusters does not changes any more. If you want to learn more about the K-means algorithm, check this out.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
DBSCAN is a density-based cluster analysis with noise. From an arbitrary starting data point, neighborhood points are specified at a distance epsilon. Clustering then begins from a certain neighborhood data point count.
The current data point becomes the first point of the new cluster, or referred to as noise. In both cases, however, it is considered to be examined. The neighboring data points are then added to the cluster. Once all neighbors have been added, a new, unexamined point is called and processed. A new cluster is thus formed.
The field of cluster algorithms is wide and everyone’s approach is different. You should be aware that there is no one solution. You have to consider each algorithm as another tool. Not every technique works equally well in every situation.
The key here is to always understand the basic approach of each algorithm you want to use. Build a small portfolio and get to know these techniques well. Once you master them, you should then add new ones. Knowing your own tools is crucial to avoid try and error and to gain control over your data. Remember: no result is a result. Your added value here is that even if an algorithm doesn’t work well on your data set, it will give you information about the data properties.
Data Mining vs Big Data Analytics – Both data disciplines, but what makes them different? In this article, we introduce you to both fields and explain the key differences.
Data Science is an interdisciplinary scientific field, as it has become more and more in focus in the last decades. Many companies see this as the key to an Industry 4.0 company. The hope is that valuable information can be found in the company’s own data, which can be used to massively increase its own profitability. Terms such as big data, data mining, data analytics and machine learning are being thrown into the ring. Many people do not realize that these terms describe other disciplines. If you want to build a house, you need the right tools and you have to know how to use them.
Map of Data Disciplines
First of all, you should think of the individual disciplines as being layered into each other like an onion. So there is overlap between all the fields and when you talk about a discipline, you are also talking about lower layers.
Since data analytics is located above data mining in the layer model, it is already clear that mining must be a sub discipline of analytics. Therefore, we will first describe the comprehensive discipline.
Data Mining vs Big Data Analytics – What is Analytics?
Big data analytics, as a sub field of data analysis, describes the use of data analysis tools and without special data processing. in data analytics, you use queries and data aggregation methods, but also data mining techniques and tools. The goal of this discipline is to represent various dependencies between input variables.
The goal of this discipline is to represent various dependencies between input variables. The following figure shows the individual overlaps in the use of the tools of the different disciplines.
Data Mining vs Big Data Analytics – What is Data Mining?
Data mining is a subset of data analytics. At its core, it is about identifying and discovering a large data set through correlations. Especially if you know little about the available data this field should be used.
But what does a typical data mining process look like and what are typical data mining tasks?
Data Mining Process
You can divide a typical data mining process into several sequential steps. In the preprocessing stage, your data is first cleaned. This involves integrating sources and removing inconsistencies. Then you can convert the data into the right format. After that, the actual analysis step, the data mining, takes place.Finally, your results have to be evaluated. Expert knowledge is required here to control the patterns found and the fulfillment of your own objectives.
The term data mining covers a variety of techniques and algorithms to analyze a data set. In the following we will show you some typical methods.
Data Mining Tasks
Besides identifying unusual data sets with outlier detection, you can also group your objects based on similarities using cluster analysis. In this article we have already summarized some popular clustering algorithms that you should know as a data scientist. While association analysis only identifies the relationships and dependencies in the data, regression analysis provides you with the relationships between dependent and independent variables. Through classification, you assign elements that were not previously assigned to classes to existing classes. You can also summarize the data to reduce the data set to a more compact description without significant loss of information.
Data Mining vs Big Data Analytics – Conclusion
Although the two disciplines are related, they are two different disciplines. Data mining is more about identifying key data relationships, patterns or trends in the data, while data analytics is more about deriving a data-driven model. On this path, data mining is an important step in making the data more usable. In the end, it’s not a versus, but both disciplines are part of an analytics pipeline. In this article, we will go further into the differences between the various data sciences and clarify the difference between data analysis and data science.
In this article we will explain everything you need to know about Apache Avro, an open source big data serialization solution and why you should not do without it.
You can serialize data objects, i.e. put them into a sequential representation, in order to store or send them independent of the programming language. The text structure reflects your data hierarchy. Known serialization formats are for example XML and JSON. If you want to know more about both formats, read our articles on the topics. To read, you have to deserialize the text, i.e. convert it back into an object.
In times of Big Data, every computing process must be optimized. Even small computing delays can lead to long delays with a correspondingly large data throughput, and large data formats can block too many resources. The decisive factors are therefore speed and the smallest possible data formats that are stored. Avro is developed by the Apache community and is optimized for Big Data use. It offers you a fast and space-saving open source solution. If you don’t know what Apache means, look here. Here we have summarized everything you need to know about it and introduce you to some other Apache open source projects you should know about.
Apache Avro – Open Source Big Data Serialization Solution
With Apache Avro, you get not only a remote procedure call framework, but also a data serialization framework. So on the one hand you can call functions in other address spaces and on the other hand you can convert data into a more compact binary or text format. This duality gives you some advantages when you have cross-network data pipelines and is justified by its development history.
Avro was released back in 2011 as a part of Apache Hadoop. Here, Avro was supposed to provide a serialization format for data persistence as well as a data transfer format for communication between Hadoop nodes. To provide functionality in a Hadoop cluster, Avro needed to be able to access other address spaces. Due to its ability to serialize large amounts of data, cost-efficiently, Avro can now be used Hadoop-independently.
You can access Avro via special API’s with many common programming languages (Java, C#, C, C++, Python and Ruby). So you can implement it very flexible.
In the following figure we have summarized some reasons what makes the framework so ingenious. But what really makes Avro so fast?
What makes Avro so fast?
The trick is that a schema is used for serialization and deserialization. About that the data hierarchy, i.e. the metadata, is stored separately in a file. The data types and protocols are defined via a JSON format. These are to be assigned unambiguously by ID to the actual values and can be called for the further data processing constantly. This schema is sent along with the data exchange via RPC calls.
Creating a schema registry is especially useful when processing data streams with Apache Kafka.
Apache Avro and Apache Kafka
Here you can save a lot of performance if you store the metadata separately and call it only when you really need it. In the following figure we have shown you this process schematically.
When you let Avro manage your schema registration, it provides you with comprehensive, flexible and automatic schema development. This means that you can add additional fields and delete fields. Even renaming is allowed within certain limits. At the same time, Avro schema is backward and forward compatible. This means that the schema versions of the Reader and Writer can differ. Schema registration management solutions exist, with Google Protocol Buffers and Apache Thrift, among others. However, the JSON data structure makes Avro the most popular choice.
What does HCA stand for? What is the difference between Agglomerative and Divisive? When do I use the algorithm and what are its strengths? In this article we will clarify all these questions.
If you don’t know what clustering means, check out this article. Here we also explain four other clustering methods that you as a data scientist must know.
What is an HCA?
Hierarchical Cluster Analysis, or HCA, is a technique for optimal and compact connection of objects based on empirical similarity measures. The two most similar objects are assigned one after another until all objects are finally in one cluster. This then results in a tree-like structure.
So how does a hierarchical cluster procedure work?
Agglomerative vs Divisive Calculation
The basic clustering can be done in two opposite ways, Agglomerative and Divisive calculation.
Agglomerative Nesting, abbreviated AGNES, is also known as the bottom-up method. This method first creates a cluster between two objects with high similarity, and then adds more clusters until all the data has been enclosed.
The divisive cluster calculation follows an opposite concept.
Divisive hierarchical clustering:
Divise Analysis, also known as DIANA, is a top-down method. All objects are directly framed into a cluster and then reduced in size.
In the following figure, the agglomerative process is compared with the divisive process.
Thus, the goal is to represent the common properties in low dimension in multidimensional raw data. A strength of this machine learning method is the inclusion of cluster relationships. With K-means, only all objects within a collection are similar to each other, while they are dissimilar to objects in other clusters. If you want to know more about this other popular clustering method, read this article.
How to calculate the cluster distances?
As mentioned earlier, not only are similarities between data points in a cluster weighted, but also similarities between groups. These similarities are represented by distances between the clusters. These distances can be determined in different ways. The distance between the centroids of two clusters can be calculated. A single linkage is the shortest distance between two clusters, a complete linkage is the largest distance between two clusters and an average linkage is the average distance between two clusters.
The figure below contrasts each cluster distance calculation method.
In addition to the planar representation, the HCA can also be represented in a dendrogram.
HCA represented in a Dendrogram
Since an HCA describes a tree structure, it can be well represented in a dendrogram. Here the connections between the individual data elements and the connections between the clusters become well visible. This diagram can help to choose the optimal number of clusters in the data depending on where you intersect the tree.
In the following figure, for example, such a dendrogram is shown in dependence on Agglomerative and Divisive Calculation.
Data Science vs Data Analysis – What distinguishes both professions from each other? How do your tasks differ? In this article, we will discuss all of these questions.
By now, almost every company, across industries and sizes, has recognized the potential in their own data. Every company wants to access this treasure and gain valuable information in order to develop profitable business strategies. The economy is crying out for experts who can manage and analyze the enormous volumes of data. A trend that is not expected to end in the next 10 years, but rather to grow steadily. So if you decide to enter the industry today and start studying, or if you want to teach yourself, you should first be clear about the differences between these often confusingly named professions. Often, HR professionals don’t even know these differences and look for the wrong profiles.
What are the similarities?
Let’s start with the similarities and the main reasons why both disciplines are often confused with each other.
Both professions deal with large amounts of data from which knowledge is to be extracted for a specific purpose. New insights are to be generated and actions are to be identified.
Map of data disciplines
In order to properly understand the relationships between the data sciences, we need to look at the following figure. The individual disciplines and their relationships to each other are shown here.
The diagram corresponds to an onion-like layering. It is important to understand that all the disciplines listed here are different. Not only are there intersections, but when you talk about a higher level discipline, it includes other, lower level disciplines.
As you can see, both data science and data analysis are ranked very high. So to understand these two disciplines you need to know the other fields as well.
What is Data Science?
When you talk about data science, you are also talking about all other data disciplines. A data scientist is an all-rounder and can apply all interdisciplinary tools and methods. He or she can handle structured and unstructured data and perform data preprocessing in addition to analysis.
What is Data Analysis?
Data analysis is more about using the right data analysis tools. Specialized data processing is not required at this level, but a data analyst must be able to fully master and understand the tools in order to gain new insights from the data.
What is Data Analytics?
Data analytics is primarily about the use of queries and data aggregation methods. The primary question here is: How can different dependencies between input variables be represented? Furthermore, this discipline makes use of data mining techniques and tools.
Data mining uses the predictive power of machine learning by applying various machine learning algorithms to big data to identify new trends in the data.
If you want to know even more about how data mining differs from data analytics, check out this article we wrote on the subject.
Data Science vs Data Analysis – So what are the differences?
So we have found that all data disciplines are similar in many ways and one discipline can imply other disciplines. In order to be able to define the differences precisely, the methods used must be compared with each other. Are programming skills required, or is the business intelligence part higher?
In the following figure, the assignment to both professions is shown once.
Both disciplines lie at the intersection of mathematics, statistics, and development. While data science is characterized by the fact that it consists of all three cornerstones, data analysis lacks the connection to computer science. And that is the biggest difference between the two fields.
Data Science vs Data Analysis – Comparison
Data Science is a branch of Big Data, with the objective of extracting and interpreting information from a huge amount of data. To do this, a data scientist must design and implement mathematical algorithms and predictive models based on statistics, machine learning, and other methods. Data Analysis is the specific application of Data Science. It specifically involves searching raw data sources to find trends and metrics. However, this involves working with larger data sets than in the area of Business Intelligence.
In the following diagram, these differences and the overlaps between the two professions are compared once again.
So what you ultimately decide to do depends on your programming interests. Do you want to develop the analyses yourself, or do you prefer to use specific analysis tools to get more value out of large data sets?
One of the most popular unsupervised clustering methods is the k-means algorithm. It is considered one of the easiest and most cost-effective clustering algorithms to create. It is therefore well suited to identify an overview of possible patterns in data.
What is the principle behind the k-means algorithm? in this article we will explain what is behind this algorithm and how it really works, because, the better you know your data science tools, the better you will be able to analyze your data.
What is k-Means?
The k-means algorithm described by MacQueen, 1967 goes back to the methods described by. Lloyd, 1957 and Forgy, 1965 described methods. The unsupervised machine learning algorithm is used for vector quantification or cluster analysis. If you don’t know what the differences are between supervised, unsupervised and reinforcement methods, read this article on the main machine learning categories.
The following figure shows the basic principle of the k-Means clustering algorithm.
The main goal of unsupervised clustering is to create collections of data elements that are similar to each other, but dissimilar to elements in other clusters.
What is the principle behind the k-means algorithm?
Here, a data set is partitioned into k groups with equal variance. The number of clusters searched for must be specified in advance. Each disjoint cluster is described by the average of all contained samples. The so-called cluster centroid. The following figure shows the cluster center of gravity principle.
Each centroid is updated to represent the average of its constituent instances. This is done until the assignment of instances to clusters does not change.
Applied algorithm process
But how exactly does the algorithm work? First, initial centroids are set. The distances between data instances and centroids are measured and data instances are added as members of the nearest centroid. The centroids are recalculated. If necessary, final centroids are re-measured, re-clustered or re-calculated
– LSMs take the temporal aspect of the input into account
– large accumulation of recurrent interacting nodes → is stimulated by the input layer – Liquid itself is not trained, but randomly constructed with the help of heuristics – Loops cause a short-term memory effect – preferably a Spiking Neural Network (SNNs) → are closer to biological neural networks than the multilayer Perceptron → can be any type of network that has sufficient internal dynamics
→ will be extracted by the readout function
– depend on the input streams they’ve been presented
– converts the high-dimensional state into the output
– since the readout function is separated from the liquid, several readout functions can be used with the same liquid
→ so different tasks can be performed with the same input