Computer Science Tutorials

DATA WAREHOUSING AND DATA MINING UNIT –I: Introduction: Why Data Mining? What Is Data Mining? What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? Which Technologies Are Used? Which Kinds of Applications Are Targeted? Major Issues in Data Mining. Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity UNIT –II: Data Pre-processing: Data Preprocessing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation, and Data Discretization UNIT –III: Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction: Working of Decision Tree, building a decision tree, methods for expressing an attribute test condition, measures for selecting the best split, Algorithm for decision tree induction. UNIT –IV: Classification: Alternative Techniques, Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks...

DATA MINING As Per the JNTU Kakinada Syllabus, tutorials will be available here S.No Description Download Links 1. Data Warehousing and Data Mining Syllabus     Click Here 2. DWDM Text Book     Click Here 3. Data Mining  Material Unit -1     Click Here Unit -2      Click Here Unit -3    Click Here Unit -4    Click Here Unit -5    Click Here Unit -6    Click Here 4. Data Mining Previous Question Papers 2016     Click Here   DWDM Powerpoint Presentation             Unit- 1                                                                Click Here             ...

Why is naive Bayesian classification called “naive”? Briefly outline the major ideas of naive Bayesian classification. Naive Bayesian classification is called naive because it assumes class conditional independence. That is, the effect of an attribute value on a given class is independent of the values of the other attributes. This assumption is made to reduce computational costs, and hence is considered “na¨ıve”. The major idea behind na¨ıve Bayesian classification is to try and classify data by maximizing P(X|Ci)P(Ci) (where i is an index of the class) using the Bayes’ theorem of posterior probability.  In general:  We are given a set of unknown data tuples, where each tuple is represented by an n-dimensional vector, X = (x1, x2. . . xn) depicting n measurements made on the tuple from n attributes, respectively A1,A2, ..,An. We are also given a set of m classes, C1,C2, . . .Cm.  Using Bayes theorem, the na¨ıve Bayesian classifier calculates the posterior pr...

Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning? The decision tree built may overfit the training data. There could be too many branches, some of which may reflect anomalies in the training data due to noise or outliers. Tree pruning addresses this issue of overfitting the data by removing the least reliable branches (using statistical measures). This generally results in a more compact and reliable decision tree that is faster and more accurate in its classification of data. The drawback of using a separate set of tuples to evaluate pruning is that it may not be representative of the training tuples used to create the original decision tree. If the separate set of tuples are skewed, then using them to evaluate the pruned tree would not be a good indicator of the pruned tree’s classification accuracy. Furthermore, using a separate set of tuples to evaluate pruning means there are less tuples to use for ...

UNIT-I What is an operating system? Describe the  Operating System functions Explain the Time- shared Operating System  Explain different categories of system calls with suitable example?  With a neat sketch, describe the services that an operating system provides to users, processes and other systems. What is the purpose of interrupts? What are the differences between a trap and an interrupt? Can traps be generated by a user program? Explain the purpose with an example. Explain briefly Layered Operating system structure with neat sketch. UNIT-II What is a process? Explain about various fields of Process Control Block.  Define a Thread? Give the benefits of multithreading. What resources are used when a thread is created? How do they differ from those used when a process is created?  Distinguish between preemptive and non-preemptive scheduling. Explain each type with an example.  What is IPC? Explain in detail the inter process commu...

Scheduling algorithms may use different criteria for selecting process from the ready list. In general,scheduling algorithm may be preemptive or nonpreemptive. Four circumstances are used for making scheduling decisions. 1. When a process switches from running state to the waiting state. 2. When a process switches from the running state to the ready state. 3. When a process switches from the waiting state to the ready state. 4. When a process terminates. Preemptive scheduling takes place for circumstances 2 and 3. Nonpreemptive scheduling takes place under circumstances 1 and 4. For 1 and 4 circumstances, scheduling is not possible and for remaining circumstance, scheduling is possible. In preemptive scheduling, a running process may be replaced by a higher priority process at any time. Preemptive scheduling is more responsive but it imposes higher overhead since each process rescheduling entails a complete process switch. In Nonpreemptive scheduling, once the CPU has been all...