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16 Data Mining Techniques: The Complete List
Latent Dirichlet allocation LDA is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words.
Figure 6. The topicmodels package takes a Document-Term Matrix as input and produces a model that can be tided by tidytext, such that it can be manipulated and visualized with dplyr and ggplot2. As Figure 6. Latent Dirichlet allocation is one of the most common algorithms for topic modeling. Without diving into the math behind the model, we can understand it as being guided by two principles.
LDA is a mathematical method for estimating both of these at the same time: finding the mixture of words that is associated with each topic, while also determining the mixture of topics that describes each document.
In Chapter 5 we briefly introduced the AssociatedPress dataset provided by the topicmodels package, as an example of a DocumentTermMatrix. This is a collection of news articles from an American news agency, mostly published around Almost any topic model in practice will use a larger k , but we will soon see that this analysis approach extends to a larger number of topics.
This function returns an object containing the full details of the model fit, such as how words are associated with topics and how topics are associated with documents. In Chapter 5 we introduced the tidy method, originally from the broom package Robinson , for tidying model objects. Notice that this has turned the model into a one-topic-per-term-per-row format.
For each combination, the model computes the probability of that term being generated from that topic. As a tidy data frame, this lends itself well to a ggplot2 visualization Figure 6.
This visualization lets us understand the two topics that were extracted from the articles. The words with the greatest differences between the two topics are visualized in Figure 6.
This helps confirm that the two topics the algorithm identified were political and financial news. Besides estimating each topic as a mixture of words, LDA also models each document as a mixture of topics.
Each of these values is an estimated proportion of words from that document that are generated from that topic. To check this answer, we could tidy the document-term matrix see Chapter 5. For example, we could collect a set of documents that definitely relate to four separate topics, then perform topic modeling to see whether the algorithm can correctly distinguish the four groups.
This lets us double-check that the method is useful, and gain a sense of how and when it can go wrong. This vandal has torn the books into individual chapters, and left them in one large pile.
How can we restore these disorganized chapters to their original books? In other applications, each document might be one newspaper article, or one blog post. As described in Chapter 5. We can then use the LDA function to create a four-topic model. This tidy output lends itself well to a ggplot2 visualization Figure 6. These topics are pretty clearly associated with the four books! Each document in this analysis represented a single chapter.
Thus, we may want to know which topics are associated with each document. Can we put the chapters back together in the correct books? Now that we have these topic probabilities, we can see how well our unsupervised learning did at distinguishing the four books. First we re-separate the document name into title and chapter, after which we can visualize the per-document-per-topic probability for each Figure 6. We notice that almost all of the chapters from Pride and Prejudice , War of the Worlds , and Twenty Thousand Leagues Under the Sea were uniquely identified as a single topic each.
It does look like some chapters from Great Expectations which should be topic 4 were somewhat associated with other topics. Are there any cases where the topic most associated with a chapter belonged to another book? One step of the LDA algorithm is assigning each word in each document to a topic. The more words in a document are assigned to that topic, generally, the more weight gamma will go on that document-topic classification.
We may want to take the original document-word pairs and find which words in each document were assigned to which topic. This is the job of the augment function, which also originated in the broom package as a way of tidying model output. While tidy retrieves the statistical components of the model, augment uses a model to add information to each observation in the original data.
This returns a tidy data frame of book-term counts, but adds an extra column:. Extra columns added by augment always start with. We can combine this assignments table with the consensus book titles to find which words were incorrectly classified.
This combination of the true book title and the book assigned to it consensus is useful for further exploration. Each row of this table represents the true book each word came from, and each column represents what book it was assigned to.
We notice that almost all the words for Pride and Prejudice , Twenty Thousand Leagues Under the Sea , and War of the Worlds were correctly assigned, while Great Expectations had a fair number of misassigned words which, as we saw above, led to two chapters getting misclassified. We can see that a number of words were often assigned to the Pride and Prejudice or War of the Worlds cluster even when they appeared in Great Expectations.
On the other hand, there are a few wrongly classified words that never appeared in the novel they were misassigned to. The LDA algorithm is stochastic, and it can accidentally land on a topic that spans multiple books. The LDA function in the topicmodels package is only one implementation of the latent Dirichlet allocation algorithm.
For example, the mallet package Mimno implements a wrapper around the MALLET Java package for text classification tools, and the tidytext package provides tidiers for this model output as well. The mallet package takes a somewhat different approach to the input format. For instance, it takes non-tokenized documents and performs the tokenization itself, and requires a separate file of stopwords. This means we have to collapse the text into one string for each document before performing LDA.
Once the model is created, however, we can use the tidy and augment functions described in the rest of the chapter in an almost identical way.
This includes extracting the probabilities of words within each topic or topics within each document. This chapter introduces topic modeling for finding clusters of words that characterize a set of documents, and shows how the tidy verb lets us explore and understand these models using dplyr and ggplot2.
This is one of the advantages of the tidy approach to model exploration: the challenges of different output formats are handled by the tidying functions, and we can explore model results using a standard set of tools. In particular, we saw that topic modeling is able to separate and distinguish chapters from four separate books, and explored the limitations of the model by finding words and chapters that it assigned incorrectly. Every document is a mixture of topics. We imagine that each document may contain words from several topics in particular proportions.
On this page 6 Topic modeling 6.
How do we extract minerals?
Coriolis Link: here. Description: A mining Corvette designed to be supported by a fleet carrier. Description: A Multi-purpose mining python that can do both laser and core mining with out any refit needed. It also has a fuel scoop to make it easier to reach those far away stations.
Type-9 or Type-10 for mining?
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Pre-Employment Medicals for the Mining Industry
Welcome to the Mining Guide! Are you fond of Minerals and Metals? Have a seat, and learn the ropes of the real Miners with our Complete Miner Guide! Why should you become a miner?
Handling Event Data
When fitted for maximum cargo capacity, which can reach tonnes, many traders find that the Type-6 becomes quite suited for trading standard commodities in bulk rather than hauling rare commodities in long jump distances. Instead, the Type 9 is one of the best ships in the game for Painite mining at an overlapping painite hotspot, due to its huge cargo. Dual painite mining will take longer than void opal mining in a Python, but will have much greater rewards. Just the basic configuration can carry 96 tons of cargo — with a couple of upgrades and tweaks, you can easily hit around tons of cargo space while maintaining your nominal C4 shields. The Lakon Type-9 Heavy is the largest ship currently available in terms of cargo capacity, with cargo units. It is the best ship for hauling freight from trade port to trade port, but because of its large size, it is very vulnerable to smaller ships.
B250 MINING EXPERT
Ship Builds. Note that all these ship builds are in Coriolis. All these builds are designed for specific activities, since typically building "general purpose" ships perform poorly at many different tasks. We'd recommend you build ships for each specific type of gameplay you want to engage in. The ships in each activity type are arranged in order from the most basic to the most advanced, both in terms of price and engineering. It may be worth reviewing the Engineering guide document. Trade Note that all of these ships are built without fuel scoops and as such are good only for short range trades.
Species C human adenovirus serotype 5 HAdV-C5 is widely used as a vector for cancer gene therapy, because it efficiently transduces target cells. A variety of HAdV-C5 vectors have been developed and tested in vitro and in vivo for cancer gene therapy. While clinical trials with HAdV-C5 vectors resulted in effective responses in many cancer patients, administration of HAdV-C5 vectors to solid tumors showed responses in a limited area. A biological barrier in tumor mass is considered to hinder viral spread of HAdV-C5 vectors from infected cells.
Mining is one of the most vital components of modern society due to the use of mined minerals in almost every consumer product. To support this need for modern equipment, mines are found in all 50 states, including our home state of Nevada. Precious minerals such as gold, copper, and silver are well-known materials mined. However, the state also extracts many other goods from the earth, including minerals used for both construction work and consumer goods.
A simple synthetic event log running-example. Note that several real event logs have been made available, over the past few years. You can find them here. The code fragment uses the standard importer iterparse, described in a later paragraph. Event logs are stored as an extension of the Python list data structure. To access a trace in the log, it is enough to provide its index in the event log. Consider the example on the right on how to access the different objects stored in the imported log.
The ORE ships listed below are grouped by size, tech level, and speciality. For each size, you progress vertically in tech level — the Tech 1 frigates leading to the Tech 2 frigates and T1 mining barges lead to the T2 exhumers. For the command ships — the horizontal progression shows the increasing size, from cruiser to capital.
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