Hash-Based Analysis : Direct Hashing And Pruning Algorithm

A set of experiments have been conducted and proved that the graph database provides a promising result. The graph approach is applied in two different methods. The sub graph approach and path finding approach. In the sub graph approach the data structures that repeat often are compared where as in path finding approach finite length search is performed. Data in the databases are written using various methods with ILP (Inductive Logic Programming) being prominent. Concept discovery involves searching for the target data given a background of facts. Association rule mining is used in relational concept discovery. Association rule mining is finding frequent patterns, associations or correlations among sets of items or objects in databases. Relational association rules are expressed as query extensions in first-order logic. Hence in the method we present a hybrid graph-based discovery of data involving both graph substructure method and path finding

“Ethnic hash” Vs “What is C.I” “Ethnic hash” and What is “Cultural Identity” both talk about cultural identity and how it affects people throughout their lives mentioning things like family traditions stereotypes and languages. “Ethnic hash” is a personal essay that uses informal voice to communicate the opinions and personal experience of the author ,it is written by Patrica. J Williams. She generally talks about food in her essay as she is preparing for a book party so she tells us how her family would make types of food such as black beans, fried rice and chicken.

Our propose approach is single database scan approach which all transactions read only one time. Initially, SIL and PTable are empty. At first time interval transaction $\left\{a,b,g,f\right\}$ is read and updates SIL with items $\left\{a\right\}$, $\left\{b\right\}$, $\left\{g\right\}$ and $\left\{f\right\}$ and set their timeset (TS) value 1 which represents occurred time. In the first row of table \ref{Figure:example1} shows SIL and PTable generated after the first timestamp. After second timestamps the SIL and PTable updated shown in second row of table \ref{Figure:example1} At timestamp three, transactions $\left\{a,b,c,e,f\right\}$ with time 3 updates TS adding time 3 and generated descriptors (D) in SIL. For an item $\left\{a\right\}$

Except where indicated, use MySQL Query Browser to perform each operation and print the results.

The output of an association rule mining algorithm is a set of association rules respecting the user-specified minsup and minconf thresholds.

Definition 10: Utility-list.: [6] The utility-list of an itemset X in a database D is a set of tuples such that there is a tuple (tid; iutil; rutil) for each transaction Ttid containing X. The iutil is the utility of X in Ttid. i.e., u(X; Ttid). The rutil is remaining utility of element X in that transaction. Two known properties of HUIs are used in this algorithm[6].

Computing frequent itemset 1: Given the database transaction id and all itemsets generate the database transaction id,itemsets format.Apply hash function to identifyy the frequent item sets ,support value and bucket count .

Examine the time required to generate Frequent itemsets with 90 percent support on pumsb [11] dataset. The comparison is shown in Table VII.

Data mining: find for more hidden relationship through without any ideas about the existence of those relationship and data that they do not collect it before.

This is similar to apriori join step. In ith level combined items generates all possible ith level transaction for Ck (candidate itemsets) that using Lk-1 itemsets. Then frequency counts are discovered for each combination and generate linked list structure and allocate items in structure.

. It uses a Merkle tree-like structure to allow for immense parallel computation of hashes for very long inputs. The design of Merkle tree is based on the claims from Intel describing the future of hardware processors with tens and thousands of cores instead of the conventional uni-core systems. With this in mind, Merkle tree hash structures exploit full potential of such hardware while being appropriate for current uni/dual core architectures. In this tree based

Before a data set can be mined, it first has to be ?cleaned?. This cleaning process removes errors, ensures consistency and takes missing values into account. Next, computer algorithms are used to ?mine? the clean data looking for unusual patterns. Finally, the patterns are interpreted to produce new knowledge.3

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

The current paper has used the association rule mining task on student database to predict the students division which is based on the previous performance. In this paper the simple Apriori algorithm has been used in WEKA for association rule generation, this may helpful to predict the future performance of the student and based on the necessary care can be taken for the students having poor

Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009).