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Over 1000 years traditional medicine has been used continuously Paper

Words: 2036, Paragraphs: 73, Pages: 7

Paper type: Essay , Subject: Medicine

Over 1000 years traditional medicine has been used continuously in Myanmar. It is very useful, valuable and powerful. Our antecedents lived healthily, wealthily and happily by only using it. Today, an evolutionary era, Myanmar Traditional Medicine should not be late. Because traditional medicine is formed by combining parts that are gained from trees, herbs and animals and not chemical products, it’s free from danger. Treatments with medicinal plants are considered very safe as there is no side effect. These remedies are fit with nature, which is the biggest advantage. This study focuses on useful traditional medicinal plants or herbs used in producing traditional medicine. In this study, FP-Growth algorithm is applied to be fast and be efficient. FP-Growth is one of frequent pattern mining comprises a set of techniques able to uncover hidden patterns from the data. The preprocessed database is mined to extract frequent patterns related to symptoms by using FP-Growth algorithm. In this study, symptoms occurred in people are taken into account from the Traditional Medicine Hospital, Mandalay, and plants’ data are collected from “Collection of Commonly Used Herbal Plants”. The accurate information of transaction data of symptoms and herbal plants is to be found in about 73% with 200 symptom transactions.

KEYWORDS: Frequent Pattern (FP) Growth, Traditional Medicinal Plants.


Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration.

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The rapid growth and integration of databases provides scientists, engineers, and business people with a vast new resource that can be analyzed to make scientific discoveries, optimize industrial systems, and uncover financially valuable patterns. This takes these large data analysis projects, researchers and practitioners have adopted established algorithms from statistics, machine learning, neural networks, and databases and have also developed new methods targeted at large data mining problems [10].

Data mining is one component of the exciting area of machine learning and adaptable computation. The goal of building computer systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. Several research communities have converged on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. Data Mining is the process of discovering new correlations, patterns, and trends by digging into large amounts of data stored in warehouses. It is related to the subareas of artificial intelligence called knowledge discovery and machine learning. Data mining can also be defined as the process of extracting knowledge hidden from large volumes of raw data i.e. the nontrivial extraction of implicit, previously unknown, and potentially useful information from data [7].


Let I = { i1, i2, …. im} be the set of items and D be the transactional data source whichcontains the set of transactions. Each transaction T is a set of items such that T?I and isassociated with an identifier called TID. An association rule is an implication of the formX=>Y, where X?I, Y?I and X_Y = _. In general, every association rule must satisfy twouser specified constraints, one is support(_) and the other is confidence (_). The support of arule X=>Y is defined as the fraction of transactions that contain X_Y, while the confidenceis defined as the ratio of support(X_Y)/support(X). An itemset is frequent if its supportsatisfies at least the minimum support, otherwise it is said to be infrequent. A frequentitemset is a Maximal Frequent itemset if it is a frequent set and no superset of this is afrequent set. The paper aims to find the Maximal Frequent itemset from a huge data source.


The solution is the frequent-pattern growth, or simply FP-growth, which mines the complete set of frequent itemsets without candidate generation. This method adopts a divide-and-conquer strategy as follows: first it compresses the database representing frequent items into frequent-pattern tree, or FP-tree, which retains the itemset association information. It then divides the compressed database into set of conditional databases; each associated with one frequent item or pattern fragmentand mines each such database separately. FP-tree is created from the root and labels it null.

The FP-growth algorithm: (mine frequent itemsets using an FP-tree by pattern fragment growth):


1. D, a transaction database.

2. min_sup, the minimum support count threshold.

Output: the complete set of frequent patterns.


(1) The FP-tree is constructed.

(2) The FP-tree is mined by calling FP-growth (FP_tree, null):

Procedure FP_growth (Tree, ?)

if Tree contains a single path P then

for each combination (denoted as ?) of the nodes in the path P

generate pattern ? U ? with support_count = minimum support count of nodes in ?;

else for each ai in the header of Tree{

generate pattern ?=ai U ? with support_count = ai.support_count

construct ?’s conditional pattern base and then ?’s conditional FP_tree Tree?;

if Tree?!= 0 then

callFP_growth(Tree?, ?); } [4].

Based on the above algorithm, association rules can be generated as follows:

For each frequent itemset l, generate all nonempty subsets of l.

For every nonempty subset s of l, output the rule “s => (l-s)” if support_count(l) / support_count(s)>=min_conf, where min_conf is the minimum confidence threshold.

Support and confidence are defined as:

Support (A -> B) = P (A? B)

Confidence (A->B) = P(A/B).

Symptom-set Implementation

Let D be a database of transaction. Eachtransaction consists of a transaction identifier and aset of symptoms {S1, S2, S3, S4, S5, …,S10, S11,……, S100,…….} selected from the universe symptom of all possible descriptive symptoms of diseases. Table 1 shows the transaction data of symptoms.

Table 3.1 Transaction Symptoms and Their Frequency

SymptomId Symptoms Support count

S018 ?????????????? 11

S048 ???????????????????????? 10

S047 ?????????????????? 10

S216 ???????????? 9

S199 ?????????????? 8

S147 ????????? 8

S014 ???????????????? 8



S204 ??????????????? 1

S252 ?????????????????? 2

There aretransactions of symptoms of diseases in this database. In the process of mining frequentitemsets. The support count of an itemset is thelength of the TID_set of the itemset. Suppose thatthe minimum transaction support count is 2.

Table 3.2 Symptoms-Frequency Table with Minimum Support Count 2

SymptomId Symptoms Support count

S018 ?????????????? 11

S185 ???????????????????????? 10

S047 ?????????????????? 10

S216 ???????????? 9

S199 ??????????????? 8

S147 ????????? 8

S252 ???????????????? 8



S253 ?????????????????? 2

In table 3.2, frequencies for each symptom are included after pruning with minimum support count 2.

FP-Growth extracts frequent symptom-sets from the FP-tree by using Bottom-up algorithm – from the leaves towards the root. It uses divide and conquer approach.

Divide and conquer:

Compress the database (build FP-tree) to retain item-sets association information.

Divides the compressed database into a set of conditional database.

Once the frequent itmesets from transaction in the database have been found, it is straightforward to generate association rules from them.

Table 3.3 Conditional Pattern-base for Frequent-Symptom Sets

SymptomID Conditional Pattern Base

S185 {S047, S186, S184: 2}

S131 {{S018, S013, S094, S199: 2}}

S261 {{S109, S111, S114: 1}, {S020, S129, S111, S114: 1}}

S160 {{S047, S186, S184, S185: 1}, {S047, S186, S184: 1}}

S147 {{S018, S146, S148, S255, S221: 1}, {S018, S146, S148, S255: 1}, {S018, S146, S148: 1}}

S252 {{S253, S251: 1}, {S253: 1}}

S253 {{S003, S090, S212, S042: 1}, {S047: 1}, {S030, S032, S049, S042: 1}}



This can be done using the following equation for the confidence, can be shown for completeness.

confidence (A ?B)=(support_count(A?B))/(support(A))

Table 3.4 Support and Confidence of Symptom-set

Subset (A) Subset (B) Sup(AUB) /Sup(A) Confidence(%)

S185 S184 2/2 100

S131 S199 2/2 100

S194 S048, S216 2/2 100

S215 S048, 216, S003 2/2 100

S041 S048, 216, S003 2/3 66.67

S261 S184 2/2 100

S090 S048, 216, S003 2/4 50

S147 S148 3/4 75




This system implemented for retrieving information of Myanmar traditional medicinal plants by FP-Growth algorithm works the following procedure. In implementing this system, a database of herbal plants on the traditional medicine is used. The database is used to send out learning. The database describes attributes of the herbalplants, such as their usage, botanical name, growing place, feature, effect and family of eachplant.

In this system, fp-growth algorithm is used together with divide and conquers approach.

System Flow Diagram

Figure 4.1 System Flow Diagram

This system focuses on the association rule mining of data mining according to the related data of Myanmar traditional medicinal plants. Firstly, data about the information of Myanmar herbal plants is stored into the herbal database. In one transaction, symptoms of disease occurred in patients are contained. By applying Frequent-Pattern growth algorithm of association rule mining, frequency of diseases of same symptoms that can be cured by the herbal plants from the transactions of symptoms are numbered with the minimum support count defined by the user-specified minimum support count and sorted by descending frequency order. There are many oriented-programming languages. Among them, this system is implemented by using Microsoft Visual Studio 2008 and Microsoft Office Access Database. This system works as follows.

Entry process for data of traditional medicinal plants, diseases, symptoms, symptoms occurred in the disease, symptoms that can be cured by the plants.

FP-tree is constructed with symptoms by the FP-growth algorithm.

Generate frequent pattern of symptoms.

Calculate confidence for symptoms of diseases in patients.

Search the suitable plants for the symptoms of diseases.

Display the related plant’s information.

When the user starts the program, the form named “Start Application” will appear. In this form, there are “System View” and “User View”. When the user chooses the “User View”, the “User View” form is going to appear. In this form, one symptom can be chosen by the user who wants symptoms can be occurred with this symptom together. In the “System View”, “File”, “FP-Growth”, and “View” menu are involved.


In this section, the processes of entering data are included in the File. These processes are “Diseases Info…”, “Pateint’s Symptom …”, “Herbal Plants Info”, “Herbal Symptom Info”, and “Exit”. In the “Diseases Info…”, common 77 diseases are stored into a table “Diseases” in the database. In this table, field names “DiseaseID” and “Disease” are included.

Symptoms that are commonly occurred in disease of patients are shown. These particular symptoms are 255 and patient’s records are totally 414 transactions. User can fill the symptoms of disease found in patients with their PatientId, Disease and Symptoms. When the user wants to view lists of data of patient’s symptoms, patient data list…” stored in database can be retrieved.

When the user wants to add or view what the symptoms can be cured by which plant, the new data about herbal plant can be added in the herbal table in the database. Plants and symptoms that can be cured by these plants are related to each other.

In all tables, the data of plant, symptoms, diseases and patient’s symptom-records can be added or deleted respectively if the user wants to add or delete these data. When the user does not want to run to continue the system, it can be closed.


Then, plant table in the database can be retrieved to view their information as knowledge containing their description, usage, location and taste. There are 120 kinds of plants taken from “Collection of Commonly Used Herbal Plants, Ministry Of Health, Department of Traditional Medicine, January 2003”.


A correlation measure can be used to augment the support-confidence framework for association rules. There are various correlations that measure to determine which would be good for mining large data sets. Lift is a simple correlation measure that is given as follows. The occurrence of itemset A is independent of the occurrence of itemset B if P(A ? B) = P(A) P(B); otherwise, itemsets A and B are dependent and correlated as events. This definition can easily be extended to more than two itemsets. If the resulting value of Equation (5.23) is less than 1, then the occurrence of A is negatively correlated with the occurrence of B. If the resulting value of a rule is greater than 1, then A and B are positively correlated, that is meaning that the occurrence of one implies the occurrence of the other. The lift between the occurrence of A and B can be measured by computing Lift (A, B) = P(A?B)/ P(A)P(B).

There are rule interestingness measures for above strong rules by lift as correlation analysis.

Table 4.3.1 Rule Interestingness Measurement

No of Transactions With Support counts and Confidence above 75% No of Rules (lift value >1) No of Rules (lift value

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