I. I NTRODUCTION The binary search tree is a well-known data structure for searching for a single key. When the keys and frequencies of successful and unsuccessful search are known, it is possible to...

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The binary search tree is a well-known data structure for searching for a single key. When the keys and frequencies of
successful and unsuccessful search
are known, it is possible to construct an optimal binary search tree. However, the
best known
algorithm to do so (Knuth, 1973) requires O(n 2 ) time and space, which is impracticable when the number of keys is large. Hence, it is worthwhile to consider constructing a nearly optimal binary search tree using a less expensive algorithm, and a number of such algorithms have been proposed by various authors. This paper evaluates empirically the performance of the following algorithms for constructing nearly optimal binary search trees:

1) Monotonic heuristic

2) Probability balanced tree

3) Min-max tree

4) Greedy tree

5) Complete tree

Answered Same DayNov 03, 2019

Answer To: I. I NTRODUCTION The binary search tree is a well-known data structure for searching for a single...

David answered on Dec 27 2019
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    Literature review
    Data Mining for IBM
The concept of IBM data mining is an action is represented as KDD or knowledge discovery in database. There are some steps of the KDD are execute before the IBM data mining, these steps are
cleaning of data, pre- processing, selection of data and transformation of the data. These techniques are used for rules of the company to detecting the dealing relationship or for the large set of data are associate between the precise values of the variables [Witten, Frank, Hall, & Pal, 2016].
For extracting the rules there will be endure a huge amount of the record is required for reading. As per the last scheme user have to repeat the entire procedure, this processor is time consuming as well as lack in efficiency. The implication of the IBM data mining action arrive which is assumed as the research topic. The main aspect to conduct study on this topic is to determine the solution for IBM data mining action for end user. This paper also has some scope of future research for new researchers.
The IBM data mining is described in the various ways. The IBM data mining is described as the function of convert the desired patterns form the large data where all the data are stored into the on line analytical process (OLAP), databases, data warehouses. The IBM data mining is also described as the KDD (Knowledge discovery in databases) [Wu, Zhu, Wu, & Ding, 2014]. From the multiple areas IBM data mining is evolve technique consumptions such as statistics, technology of the database, learning machines, retrieval of information neural networks, etc. There are some major components of the IBM data mining architecture are as given below:
1. Database,
2. Data warehouse
3. Other information repository
Many of the IBM data mining techniques are explained in this paper, discuss that how they can deal with other database and these techniques are used to abstract the information and they can expressed the frame of the multi basic IBM data mining. There are three needs of the goal to search the data from a precise database system.
The desired data is extracted by the information which is consisting in the database. The data of the IBM mining is developed in the form of decision making process. All the data of the IBM mining and the data which is mined are obsessed each and every operation of the decision making. The literature reviews includes the some of the process consistent of the IBM data mining. There are some processes consist by the IBM data mining are as given below [Grossman, Kamath, Kegelmeyer, Kumar, & Namburu, 2013]:
1. Analysis of the time series: Analysis of the time series is the action in which statistical techniques are used and a series of the time dependent of the data points are explained.
2. Analysis of association: The main purpose of the analysis association is to calculate the patterns in to the process of business and prepare the relevant rules.
3. Classification: They are classified in Decision Trees, Basic Concepts, and Model Evaluation.
4. Regression: There are some products which are predicted by the regression sales, value of value, square footage, mortgage rates, profit, distance or the temperature.
5. Analysis of the cluster: Analysis of the cluster are used to determining the group objects such as the group are same in the object to each other and different from the other groups in the object.
6. Summarization: Summarization is the concept of IBM data mining key which is developing the compact description of the dataset from evolving some techniques.
Mining and Process management for static data: Mining Process for IBM
The IBM data mining process for convert the data warehouse contents into the instruction that can be complex for the process of decision-making, this process can be explained as below:
Selection of the Data
There are many types of the diverse data contained by a data warehouse, and this data is not mandatory for achieving the goal of IBM data mining. First type of the IBM data mining process is selection of data. For example, to described the purchase of the customer a database is mandatory to contain by a market, data for demographic, data for lifestyle, Data for finance, etc.
It is formed by combining the purchase data of customer with the data of demographic and the executive of market, for analyzing the counter of the department store. The chosen data will be formed onward of the table. For the selection of data, a table is require to join the product like Datajoiner(*) and IBM(*).
Moreover to analyze the data of the table, it is not mandatory to mining always the whole table data by selecting the database of the given...

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