⭐⭐⭐⭐⭐ Information Mining Research Paper

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Information Mining Research Paper

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Association rule mining algorithms ! Review of data mining research paper

One of the major purposes of the Data Mining is a visual representation of the results of calculations, which allows Data Mining tools be used by people without special mathematical training. At the same time, the application of the data analysis statistical methods requires a good knowledge of the probability theory and mathematical statistics. Nowadays, high technologies are taking more and more important role in the process of taking the most important decisions. Information processing by super-powered cluster computers yield results comparable to usefulness of mining. The students, who write their research paper on data mining, have to by all means take into account the value of the knowledge discovery processes in the modern world.

It is very important to identify carefully all the methods that make up the process of data mining, to navigate freely in the wilds of this complicated issue. Your own ideas on how to further develop the technology, which in the future will be more and more widespread, will certainly be appreciated. To write a good research paper on data mining as well as data warehousing, the investigators should focus on comparing the critical components that compile the totality of the knowledge discovering methods.

You should be able to analyze all the nuances that can be recognized only by painstaking inspection. Apriori will be very low and inefficient when memory capacity is limited with large number of transactions. The proposed approach in this paper reduces the time spent for searching in the database and performing transactions for frequent itemsets and also reduces the memory space with large number of transactions using partitioning and selecting which is described in detail in the proposed model. Apriori Algorithm takes a lot of memory space and response time since it has exponential complexity eg; if there are transactions then it will have itemsets and it also does mining twice.

We can somehow reduce the itemsets by frequent itemsets mining FIM then it will significantly reduce the time taken but it will take a lot of space, and it will be very inefficient for real time applications eg; if a grocery seller wants to know about the most frequent items purchased or if a person wants to know about the books which are read most frequently in the library, they will have to format their systems again and again as it takes a huge memory space for storing candidate and frequent itemsets.

So what can be the solution to minimize it? Also can we minimize the running time of the Algorithm further by using a different approach? Explain the approach. This section will address the improved Apriori ideas, the improved Apriori, an example of the improved Apriori, the analysis and evaluation of the improved Apriori and the experiments. In the process of Apriori, the following definitions are needed:. In our proposed approach, we enhance the Apriori algorithm14 to reduce the time consuming for candidate itemset generation. We first scan all the transactions to generate F1 which contains the items, their support count and. Transaction IDs where the items are found. And then we use F1 as a helper to generate F2, F3.

Before scanning all the transaction records to count the support count of each candidate, use F1 to get the transaction IDs of the minimum support count between x and y, and thus scan for C2 only in these specific transactions. The same thing applies for C3, construct 3- itemset C x, y, z , where x, y and z are the items of C3 and use Fi to get the transaction IDs of the minimum support count between x, y and z and then scan for C3 only in these specific transactions and repeat these steps until no new frequent itemsets are identified. Now to reduce the memory space when large transactions are there a simple rule can be followed: Let n be the number of nodes in the FP- tree and k be the color of the clusters of the transactions in the database.

If this is the case then k is at most n - 1. Suppose we have transactions then k will be at most There are so many possibilities of colors and all the colors will be chosen by us. Well, clearly that leads to a bad choice. It must be same if the tree is fully dependent. Since it takes exponential memory space, the possibilities of colors getting generated should be minimized. This can be done by using another mathematical formula for comparing the number of nodes and colors i. The base 2 signifies that the cluster is getting partitioned into 2 parts and selecting means out of the two only j is getting selected. This can be any number of partitions depending on user's choice. User will be having the choice of deciding the base. The value of the base is equal to the number of partitions of the cluster.

Using this approach very less memory space is consumed at a time and items can be mines in a lesser amount of time. Hence, it serves the purpose. Now, select clusters one at a time. Assume that a large supermarket tracks sales data by stock- keeping unit SKU for each item, such as "butter", "bread", "jam", "coffee", "cheese", "milk" is identified by a numerical SKU. The supermarket has a database of transactions where each transaction is a set of SKUs that were bought together.

The transaction set as shown in Table 1. The frequent. The next step is to generate candidate 2-itemset from L1 split each itemset in 2-itemset into two elements then use ll table to determine the transactions where you can find the itemset in, rather than searching for them in all transactions. For example, let's take the first item in table. After that we search for itemset Milk, Cheese only in the transactions T1 the minimum confidence, and then generate all candidate association rules. In the previous example, if we count the number of scanned transactions to get 1, 2, 3 -itemset using the original Apriori and our improved Apriori, we will observe the obvious difference between number of scanned transactions with our improved Apriori and the original Apriori.

From the table 6, number of transactions in1-itemset is the same in both of sides, and whenever the k of k-itemset increase, the gap between our improved Apriori and the original Apriori increase from view of time consumed, and hence this will reduce the time consumed to generate candidate support count. To get support count for every itemset, here. Cheese, and T7. For a given frequent itemset LK, T4, find all non-empty subsets that satisfy the minimum confidence, and then generate all candidate association rules. The final output of the FP-Tree is as shown in Graph And the minimum support count is 3.

Now we will find the frequent patterns from the FP-Tree. It's trivial. The items of the database and their frequency of occurrences is shown in Table:2 for each item. First and foremost, we need to prioritize all the itemsets according to their frequency of occurrences and then we will see each item one by one from bottom to top. The items can be listed as: Then we see Jam. First we need to find the conditional pattern base for Jam If you wonder how 3 comes, it is due to frequency of occurrence of Jam.

Now go to Graph 1 and check the Jams. There are 3 Jams and one occurrence for each. Now traverse bottom to top and get the branches which have Jams with the occurrence of Jam. To ensure that you correctly got all the occurrences of Jam in FP-Tree add occurrences of each branch and compare with the occurrences listed above. Then we consider Cheese. And this way we can ensure the correctness for all. Assume every user in this website has a shopping basket that can be edited at any time. If the shopping basket contains products when the user leaves the website, the user basket's status is saved and is retrieved when the user enters the website again.

Possible user actions are described by the WF-net shown in Figure 4. Now, assume we do not know the net in Figure 4, but we do have a complete log of acceptable audit trails. Given WOK as input, the a-algorithm discovers the net shown in Figure 4. Once the net is discovered, the conformance of every new audit trail can be verified by playing the "token game". Note that anomalous audit trails do not correspond to possible firing sequences in the "token game" for the discovered net.

Furthermore, the "token game" detects the point in which the audit trail diverges from the normal behavior and allows also for the real time verification of trails. The first audit trail in is an acceptable one. Note that this trail is not in ,. The second trail is an anomalous one because it does not contain the task Provide Password. By playing the "token game", we see that two tokens get stuck in the input places of Provide Password. In other words, the "token game" explicitly shows the point where the anomalous behavior happened. The EMiT tool [15] supports the "token game" and indicates deadlocks and remaining tokens.

Note that the a-algorithm correctly discovered the net in Figure 4 without requiring the "training" log to show all possible behavior the first trace in is not in , although is complete and the first trace at. However, because the a-algorithm aims at discovering the process perspective, it does not capture constraints that relate to data in the system, like the maximum number of times a loop may iterate. For the example in Figure 4, the loop can be executed an unlimited number of times without violating security issues. Nonetheless, if the loop would correspond to user attempts to log into the system, a maximum number of loop iterations must be set.

If this is the case, the discovered WF-net must be explicitly modified to incorporate the required data-related constraints. As a final remark, we would like to point out that the simple idea of playing the "token game" can also be used without applying the a-algorithm, i. However, given the evolving nature of systems and processes, the a-algorithm is a useful tool to keep the "security process" up-to-date. For example, if an audit trail "does not fit" but does not correspond to a viola-. Audit trails that seemed OK, but turned out to be potential security breaches can be removed from the log. By applying the a-algorithm to the modified event log, a new and updated "security process" can be obtained without any modeling efforts. The ordering relations can be used to check system properties.

In Section 4, a process model is derived from acceptable audit trails. The discovered net is then used to check new audit trails. In this case, every audit trail must comply with the process. However, sometimes security applies only to a part of the process. For example, for the process in Figure 4, the critical security issue is to execute the task Provide Password before Process Order. In other words, task Provide Password should cause task Process Order. The process fragment for this situation is construct a in Figure 5. This construct is mapped to the ordering relation Provide Password eProcess Order. Thus, given an audit log, we can check if this pattern holds for the system. Thus, we can conclude that the process described by W contains the pattern shown in Figure 5 a.

The approach to check process conformance verifies if a pattern holds, but does not assure this is always the case. Full conformance can be verified by combining this approach with the one in Section 4. The difference is that we now play the "token game" with the subnet. By playing every event trace in the desired pattern, we check if there is always a causal relation between Provide Password and Process Order.

Note this will not be the case for all. The main advantage of the approach for checking process conformance is that it does not require a complete audit log for the whole process, but only for the tasks involved in the pattern. Figure 5 illustrates the basic patterns that can be used to build process fragments. The idea of process mining is not new [2,5,7,8,13,23,24,26,29,33,36] and most techniques aim at the control-flow perspective.

However, process mining is not limited to the control-flow perspective. For example, in [4] we use process mining techniques to construct a social network. For more information on process mining we refer to a special issue of Computers in Industry on process mining [6] and a survey paper [5]. In this paper, unfortunately, it is impossible to do justice to the work done in this area.

The focus of this paper is on the a-algorithm. For more information on the algorithm, we refer to [2,7,26,36]. In [27] one of the problems raised in [26] is tackled "short loops" and should be considered as an extension of [7]. In the security domain there are related papers dealing with intrusion detection based on audit trails [19,37]. These paper break "normal behavior" into smaller patterns and then compare actual audit trails using these patterns.

Note that, unlike the a-algorithm, these approaches do not consider explicit process models. There have been many formal approaches towards security, e. Unlike our approach they typically focus on verification of a design rather than analyzing the actual behavior. For more details we refer to www. In this paper, we explored the application of process mining techniques in security. First, we introduced process mining and then we focused on one algorithm to mine the process perspective.

Then we showed the application of this algorithm to security issues. First we discussed the detection of anomalous process executions in the mined WF-net by playing the "token game" for concrete cases. Then, we showed that process conformance can be checked by comparing process fragments with the discovered WF-net. The focus on Corporate Governance and governmental regulations such as Sarbanes-Oxley Act trigger the development of tools to enforce and check security at the level of business processes.

We believe that organizations will increasingly need to store and monitor audit trails. Process mining techniques such as the a-algorithm can assist in these efforts. The Journal of Circuits, Systems and Computers, 8 1 —66, Han, S. Tai, and D. Springer-Verlag, Berlin, Workflow Management: Models, Methods, and Systems. Mining Social Networks: Uncovering interaction patterns in business processes. Weske, B. Pernici, and J. Herbst, L. Maruster, G. Schimm, and A. Data and Knowledge Engineering, 47 2 , Weijters, editors. Elsevier Science Publishers, Amsterdam, Weijters, and L. Bernard, P. Killworth, C.

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