Aop 1.Kol

Visualization of data is very important for data science because it allows researchers and analysts to easily understand and interpret complex patterns and relationships within the data. By representing data visually, it becomes easier to identify trends, outliers, and anomalies, which can lead to valuable insights and informed decision-making. Additionally, visualizations can help communicate findings to stakeholders in a clear and intuitive manner, enhancing the overall impact and effectiveness of data analysis.

The correct answer is "Redosled je bitan." This means that the order of sequential data is important and changing the order can alter the meaning or significance of the data.

Sequential data is characterized by the fact that the order or sequence of the data is important. This means that changing the order of the data can alter its meaning or interpretation. Therefore, in this case, the correct answer is "Redosled bitan" which translates to "Order is important."

The correct answer is "Linearnu zavisnost" which translates to "Linear dependence" in English. The coefficient of correlation measures the strength and direction of the linear relationship between two variables. It indicates how closely the data points in a scatter plot cluster around a straight line. A high correlation coefficient indicates a strong linear relationship, while a low correlation coefficient suggests a weak or no linear relationship. Therefore, the coefficient of correlation measures linear dependence between variables.

Hijerarhijsko klasterovanje formira skup ugnezdenih klastera organizovanih u obliku drveta. This means that the clusters are organized in a hierarchical structure, where each cluster is nested within another cluster, forming a tree-like structure. This allows for a clear understanding of the relationships and similarities between different clusters, as well as the ability to easily navigate and analyze the data.

The given correct answer is "Diskretan atribut". This means that the set of words in a document is a discrete attribute. A discrete attribute is a type of attribute that has a finite or countable number of distinct values. In this case, the set of words in a document can be counted and each word is distinct from the others. Therefore, it can be categorized as a discrete attribute.

Hijerarhijsko klasterovanje se vizualizuje u obliku dendograma. Dendogram je grafički prikaz hierarhijske strukture klastera, koji se sastoji od spojenih linija koje predstavljaju sličnost između klastera. Na x-osi se nalaze objekti koji se klasteruju, dok se na y-osi nalazi mera udaljenosti između klastera. Dendogram omogućava vizuelno prikazivanje procesa klasterovanja i identifikaciju grupa objekata koje su slične jedna drugoj.

A simple model with a large amount of training data is better than a complex model with fewer training data because having more data allows the model to learn more effectively and make more accurate predictions. The simplicity of the model also helps in reducing overfitting and improves generalization. Therefore, the statement "Tacno" (True) is correct.

The given question is asking about the nature of temperature. The answer "Kontinualan atribut" suggests that temperature is a continuous attribute. This means that temperature can take on any value within a certain range and can be measured with precision. It is not limited to specific discrete values or categories.

The correct answer is "Non-zero attribute values." This means that only the attribute values that are not equal to zero are important in this context.

The correct answer suggests that cluster analysis involves finding groups of objects that are similar within each group but different from objects in other groups. This means that objects within a cluster share similarities among themselves but are distinct from objects in other clusters.

A good representative sample has approximately the same characteristics as the original data. This means that the sample accurately reflects the population from which it is drawn, allowing for valid inferences and generalizations to be made about the population based on the sample.

The correct answer is "Da jedna strana dokaze da poseduje neku tajnu i da je pri tom dokazivanju ne otkrije." This means that the Zero Knowledge Protocols allow one party to prove that they possess a secret without revealing the secret itself during the proof.

Dimensionality reduction reduces the need for long training sets because it reduces the number of features or variables in the data. It also facilitates visualization by reducing the data to a lower-dimensional space that can be easily visualized. Additionally, it enables better performance of machine learning algorithms as it removes irrelevant or redundant features, allowing the algorithms to focus on the most important information.

The correct answer is "g(X)>0" because if the value of g(X) is greater than 0, it means that the discriminant function for class 1 is larger than the discriminant function for class 2. This indicates that the input X belongs to class 1, and therefore, we decide to classify it as class 1.

By selecting a representative sample, the number of data to be processed is reduced. This is because a representative sample is a smaller subset of the entire population or dataset that accurately represents the characteristics and diversity of the whole. By analyzing this smaller sample, researchers can make inferences and draw conclusions about the larger population, saving time and resources compared to processing the entire dataset. Therefore, the correct answer is that selecting a representative sample reduces the number of data to be processed.

Aggregation is an operation that combines two or more attributes or objects. It is a process of creating a new entity by combining existing attributes or objects. In this case, the correct answer includes both options of combining two or more attributes and combining two or more objects.

The given answer suggests that if the measure of dissimilarity between two feature vectors is equal to 1, it means that the two feature vectors are maximally different from each other.

Bajesovo odlučivanje u teorijskom smislu obezbeđuje minimalnu grešku odlučivanja. Bajesova teorija se zasniva na Bayesovom teoremu koji koristi statističke metode za donošenje odluka na osnovu raspoloživih podataka. Ova metoda uzima u obzir verovatnoću pojedinih događaja i koristi ih za procenu najbolje odluke. Kada se optimalno primeni, Bajesovo odlučivanje može minimizirati greške odlučivanja i obezbediti najbolji mogući rezultat.

The correct answer states that we choose the class omega_i if the probability P(omega_i) is greater than the probability P(omega_j) for every j, where j is different from i. This means that we select the class with the highest probability among all the classes.

When the dimensionality of data increases, it is necessary for the data to become more sparse in the space they occupy. This means that the data points become more spread out and move away from the coordinate origin. As the dimensionality increases, the data points tend to become more scattered and spread out, leading to sparsity in the space they occupy.

The correct answer suggests that when the similarity measure between two feature vectors is equal to 0, it indicates that the two vectors are maximally different. This means that there is no similarity or overlap between the features of the two vectors.

Dimensionality reduction reduces the need for long training sets by reducing the number of features or variables in the data. This makes it easier to visualize the data as it reduces the complexity and allows for easier interpretation. Additionally, dimensionality reduction enables better performance of machine learning algorithms as it reduces noise and redundancy in the data, leading to improved accuracy and efficiency.

The correct answer is "Particiono klasterovanje." This refers to partition clustering, which involves dividing the data into non-overlapping subsets or partitions. Each partition represents a cluster, and each data point belongs to only one cluster. This method is different from hierarchical clustering, where clusters are formed based on a hierarchy of nested clusters, and from mixed clustering, which combines different types of clustering algorithms.

The correct answer is "Utice" which means "It does influence" in English. This implies that the choice of initial centroids in the K-means algorithm does have an impact on the final solution. The initial centroids determine the starting points for the clustering process, and different initial choices can lead to different cluster assignments and ultimately different final solutions.

The correct answer is "Klase se ne preklapaju" which translates to "Classes do not overlap" in English. This means that the classes in the classification problem are distinct and do not have any common elements. Each data point belongs to only one class and there is no ambiguity or overlap in assigning the data points to their respective classes.

The correct answer is P(X). In probability theory, P(X) represents the probability of event X occurring. It is a measure of the likelihood of X happening. In this context, "evidens" refers to the evidence or information available. Therefore, P(X) represents the probability of event X given the available evidence.

In the context of data clustering in Euclidean space, the measure of quality used is the criterion of the difference of squared errors. This means that the quality of the clustering is evaluated based on the difference between the actual values and the predicted values, with the errors being squared to emphasize the importance of larger errors. The larger the difference of squared errors, the worse the clustering quality, as it indicates a larger discrepancy between the predicted and actual values. Therefore, this criterion is used to assess the effectiveness of clustering algorithms in minimizing errors.

Autlajeri su objekti koji su značajno različiti od najvećeg broja objekata u datom skupu podataka. This statement suggests that autlajeri are significantly different from the majority of objects in the given dataset.

The answer "Nema" suggests that it does not make sense to search for a decision rule that gives even smaller error than the known minimum Bayesian error. This implies that the minimum Bayesian error is already the best possible error rate that can be achieved, and there is no point in looking for a better decision rule.

Na imenske atribute se može primeniti operacija "Različitosti". Ova operacija se koristi za poređenje vrednosti dva ili više imenskih atributa kako bi se utvrdilo da li su oni različiti ili ne. Na primer, možemo uporediti vrednosti atributa "boja" i "veličina" da bismo videli da li su različiti.

In hierarchical clustering, it is not necessary to predefine the number of clusters. This is because hierarchical clustering builds a tree-like structure (dendrogram) where the number of clusters can be determined by cutting the dendrogram at a desired height. This allows for flexibility in choosing the number of clusters based on the specific requirements of the analysis. Therefore, the correct answer is "No".

Nakon dobijanja dendograma u hijerarhijskom klasterovanju, zeljeni broj klastera se dobija skracivanjem dendograma na odgovarajući nivo. Ovo se može postići postavljanjem praga na odgovarajuću visinu u dendogramu, gde se presecaju grane koje predstavljaju klasterovanje. Na taj način se formiraju klasteri prema zadanom broju.

The correct answer states that x belongs to class j if g_j_(x) is greater than g_i_(x) for all i=1,2..,C, and different from j. This means that the discriminant function g_j_(x) for class j should have a higher value than the discriminant functions g_i_(x) for all other classes i, indicating that x is most likely to belong to class j.

The correct answer states that data visualization is a transformation of data from an initial dimensionality N to a dimensionality of 3, where N is greater than 3. It also states that data can be transformed from an initial dimensionality N to a dimensionality of 2, where N is greater than or equal to 3.

By changing the decision rules, we can redesign the system in a way that reduces its error below the established minimum Bayesian decision error. This means that by modifying the criteria or conditions used to make decisions within the classification system, we can improve its accuracy and reduce the likelihood of making incorrect classifications.

The correct answer is "Skup stavki" because the given statement mentions that each record in transaction data contains a set of items. This implies that the transaction data is organized in such a way that multiple items are associated with each record.

In each step of divisive hierarchical clustering, clusters are divided. This means that the existing clusters are split into smaller clusters based on some criterion, such as distance or similarity. This process continues until each data point is assigned to its own individual cluster.

In each step of the hierarchical agglomerative clustering, the algorithm combines the pair of clusters that are closest to each other. This means that the algorithm starts with individual data points as clusters and then iteratively merges the two closest clusters until all data points belong to a single cluster. This approach allows the algorithm to create a hierarchy of clusters, where each level represents a different level of similarity or distance between the clusters.

The given answer, P(X|omega_i), refers to the likelihood of class omega_i given the data X. This term represents the probability of observing the data X, given that the true class is omega_i. In other words, it quantifies how well the data X is explained by the class omega_i. It is an essential component in various statistical and machine learning algorithms, such as Naive Bayes and Maximum Likelihood Estimation, for estimating class probabilities and making predictions.

When the set of objects we have is small, it is more reasonable to estimate the missing values rather than replacing them with all possible values or eliminating the objects. Estimating the missing values allows us to make educated guesses based on the available data, which can provide more accurate results compared to the other methods.

Agregirani podaci imaju tendenciju da imaju manja odstupanja. This means that when data is aggregated, the variations or differences between the individual data points are reduced. Aggregation involves combining multiple data points into a single value or summary, which can help to smooth out any outliers or extreme values. Therefore, the aggregated data tends to have smaller deviations or discrepancies compared to the unaggregated data.

The correct answer includes different types of sampling techniques. "Izbor uzorka po slojevima (delovima)" refers to stratified sampling, which involves dividing the population into different strata or layers and then selecting a sample from each stratum. "Izbor uzorka sa zamenom" refers to random sampling with replacement, where each member of the population has an equal chance of being selected multiple times. "Jednostavni slucajni uzorak" refers to simple random sampling, where each member of the population has an equal chance of being selected. "Izbor uzorka bez zamene" refers to random sampling without replacement, where each member of the population can be selected only once. These different sampling techniques allow researchers to obtain representative and unbiased samples for their studies.

At the beginning of agglomerative hierarchical clustering, each vector feature is considered as a separate cluster. This means that initially, each vector is treated as its own cluster. As the clustering algorithm progresses, these individual clusters will be merged together based on their similarity until a final clustering solution is obtained.

In the criterion of sum of squared errors in cluster analysis, the error is determined as the distance of each feature vector to the center of the nearest cluster. This means that the error is calculated by measuring the distance between each data point and the centroid of the cluster it belongs to. The closer the data point is to the centroid, the smaller the error will be.

The correct answer is "Uredjenja, Razlicitosti." This means that the operation of ordering and the operation of distinctness can be applied to ordinal attributes. In other words, ordinal attributes can be arranged in a specific order and can have different values.

In classification problems, the concept of class completeness means that every sample that undergoes classification must belong to one of the defined classes. This implies that there should be no samples that do not fall into any class or belong to multiple classes. Class completeness ensures that all samples are properly categorized and accounted for in the classification process.

The correct answer is "Apriorne verovatnoce klase". This refers to the prior probabilities of each class in a generative model. These probabilities represent our knowledge or assumptions about the likelihood of each class occurring before seeing any data. They are used in calculating the posterior probabilities, which are updated probabilities after observing the data. In a generative model, these prior probabilities are used along with the likelihood function to estimate the class membership probabilities for new data points.

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The correct answer is "Karakteristike, Svojstva, Obelezja." In the field of data science, "atributi" refers to the characteristics or properties of objects or instances. These attributes can be used to describe or differentiate different data points or objects. Therefore, "Karakteristike, Svojstva, Obelezja" are all synonyms for "atributi" in this context.