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**Authors :**Akshay A. Bandivadekar , Harshal D. Gadhia , V.Mukhilan -
**Paper ID :**IJERTCONV2IS04033 -
**Volume & Issue :**ICONET – 2014 (Volume 2 – Issue 04) -
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**ISSN (Online) :**2278-0181 -
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#### Comparison of Clustering Algorithm in Automated Inventory System

Comparison of Clustering Algorithm in Automated Inventory System

Akshay A. Bandivadekar , Harshal D. Gadhia ,V.Mukhilan,

Abstract Patterns and classification of stock or inventory data is very important for decision making and business support. In proposed system an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products, Identification of sales patterns from inventory data indicate the market trends which can further be used for forecasting, decision making and strategic planning. The objective is to get better decision making for improving sales, services and quality as to identify the reasons for dead stock, slow moving and fast moving stock. The system proposes two phases in which first phase includes initial clustering which is performed on the database with the help of a clustering algorithm. In the second phase the system uses most frequent pattern, MFP algorithm to find the frequencies of property values of the items. The existing system uses k-means clustering algorithm along with MFP for mining patterns. In order to improve the execution time the proposed system uses efficient methods for clustering which includes Partitioning Around Medoids, PAM and Balanced Iterative Reducing and Clustering using Hierarchies BIRCH along with MFP.The most efficient iterative clustering approach called as PAM is used for initial clustering and is then combined with frequent pattern mining algorithm. In order to meet the memory requirements, an incremental clustering algorithm BIRCH is also used for mining frequent patterns. So, the evaluation of these clustering algorithms along with MFP is made with respect to the execution times.

Keywords: Data mining, Clustering, K-means, PAM, MFP, computational complexity

INTRODUCTION

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics

Association algorithms are designed to operate on databases containing transactions. As is common in association rule mining, given a set of item sets, the algorithm attempts to find subsets which are common to at least a minimum number C of the item sets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested

against the data. The algorithm terminates when no further successful extensions are found.

The relationship among the large amount of biological data has become a hot research topic. It is desirable to have clustering methods to group similar data together so that, when a lot of data is needed, all data are easily found in close proximity to some search result.

Clustering techniques have a wide use and importance nowadays. This importance tends to increase as the amount of data grows and the processing power of the computers increases. Clustering applications are used extensively in various fields such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing.

The main purpose of clustering techniques is to partitionate a set of entities into different groups, called clusters. These groups may be consistent in terms of similarity of its members. As the name suggests, the representative-based clustering techniques uses some form of representation for each cluster. Thus, every group has a member that represents it. The motivation to use such clustering techniques is the fact that, besides reducing the cost of the algorithm, the use of representatives makes the process easier to understand. There are many decisions that have to be made in order to use the strategy of representative-based clustering. For example, there is an obvious trade-off between the number of clusters and the internal cohesion of them. If there are few clusters, the internal cohesion tends to be small. Otherwise, a large number of clusters makes them very close, so that there is little difference between adjacent groups. Another decision is whether the clusters should be mutually exclusive or not, that is, if an entity can co-exist in more than one cluster at the same time.

BACKGROUND AND RELATED WORK

For the transportation industry hugely contributes to the economy of India.For transportation purpose, the development and management of highway is a must. So tries to cluster research objects (namely chosen hub city) using clustering analysis , and then sort them according to some rules in order to make sure different layers of highway transportation cities

sets[1]. Data is very important for every organization and

business. Data mining techniques like clustering and associations can be used to find meaningful patterns for future predictions. Clustering is used to generate groups of related patterns, while association provides a way to get generalized rules of dependent variables. The algorithms used are K- means, MFP(Most Frequent Pattern), K-Medoid, Birch

algorithm [13].To formulates, simulates and assess an improved data clustering algorithm for mining web documents with a view to preserving their conceptual similarities and eliminating problem of speed while increasing accuracy. The proposed algorithm was simulated using the fuzzy logic and statistical toolbox in Matlab7.0 [4] . To represent a data mining approach for inventory forecasting and planning a Bill of Materials in a highly competitive environment such as an Italian car racing team. By exploiting clustering algorithms and by using statistical techniques to identify the optimal number of clusters this work presents a method to optimally cluster a multi-year dataset containing the products used in car revision after each rally competition during a three year period

[9] .Clustering Algorithm:

K-Mean algorithm.

K- Medoids algorithm (PAM)

Birch Algorithm

Association Rule Mining Algorithm:

Most Frequent Pattern (MFP) algorithm.

Apriori Algorithm

Comparison will be done by using two parameters Number of Iterations and size of dataset handled by each algorithm.

Our aim is to find out which algorithm is best and time efficient among clustering algorithms and which algorithm works best with which association algorithm.

Stock market produces huge datasets that deals enormously complex and dynamic problems with data mining tool. Data mining is the emerging methodology used in stock market, finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decision. [12] .To obtained the frequent patterns from the stock data. Hybrid clustering association mining approach is proposed to classify stock data and find compact form of associated patterns of sale . From the experimental results it is clear that proposed approach is very efficient for mining patterns of stock data with less computational time than the proposed approach .By these patterns we may predict the

METHODOLOGY 1)Clustering Algorithm:

K-Mean Algorithm

3 categories

/li>

Dead-Stock (DS).

Slow-Moving (SM).

Fast-Moving (FM) .

Start

factors affecting the sales. In future we may try to implement the same process in document classification and may even try

to have better computational efficiency by efficient algorithms

[17].Number of cluster K

EXISTING STSTEM

In paper [17], an algorithm is used for mining patterns of huge stock data to predict factors affecting the sale of products. To achieve these goals, we need to fully exploit this data by extracting all the useful information from it.

The algorithms used are 1. K-Mean algorithm.

K- Medoids algorithm (PAM)

Birch Algorithm

Most Frequent Pattern (MFP)

In this paper, for clustering K-Mean algorithm, K- Medoids algorithm (PAM), Birch Algorithm are used and Association is done by using Most Frequent Pattern (MFP) algorithm.

In this paper, comparison done by using Number of Iteration

This paper shows that Birch with MFP algorithm shows best result as compared to K-Mean with MFP algorithm and K-Medoids with MFP algorithm.

PROPOSED SYSTEM

Only through data mining it is possible to extract useful pattern and association from the stock data.

Algorithm are used:-

Centroid

Distance object to Centroid

Grouping based on Minimum distance

Fig.K-Mean Algorithm

No object move

End

over all medoids

Steps for the K-Mean algorithm

Step 1: Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.

Step 2: Assign each object to the group that has the closest centroid.

Step 3: When all objects have been assigned, recalculate the positions of the K centroids.

Step 4: Repeat Steps 2 and 3 until the centroids no longer move.

The PAM Clustering Algorithm

Start

Number Of cluster center k and medoids k

Group objects to closest medoid

Swap each medoid and non medoid object

Select minimum cost

NO

No change

YES

end

Figure 1: Flowchart of PAM algorithm

PAM (Detail Algorithm given by Margaret H.Dunham):- Arbitrarily select k medoids from D;

Repeat

for each th not a medoid do for each medoidt,do calculateTCih;

Find i,h where TCih is the smallest;

If TCih<0 then replacemedoidti with th; Until Tcih<=0;

for each ti belonging to D do

assignti to kj where dis(ti,tj) is smallest

BIRCH: An Efficient Data Clustering Method for Very Large Database

Introduction:

Definition of Data clustering

Given the desired number of clusters K and a distance- based measurement function, we are asked to find a partition of the dataset that minimizes the value of the measurement function.database-oriented constraint:

The amount of memory available is limited and we want to minimize the time required for I/O.

Insertion into a CF Tree

We now present the algorithm for inserting an entry into a CF tree.Given entry Ent, it proceeds as below:

Identifying the appropriate leaf: Starting from the root, according to a chosen distance metric D0 to D4 as defined before, it recursively descends the CF tree by choosing the closest child node .

Modifying the leaf: When it reaches a leaf node, it finds the closest leaf entry, and tests whether the node can absorb it without violating the threshold condition.

If so, the CF vector for the node is updated to reflect this.

If not, a new entry for it is added to the leaf.

If there is space on the leaf for this new entry, we are done.

Modifying the path to the leaf: After inserting Ent into a leaf, we must update the CF information for each nonleaf entry on the path to the leaf.

In the absence of a split, this simply involves adding CF vectors to reflect the additions of Ent.

A leaf split requires us to insert a new nonleaf entry into the parent node, to describe the newly created leaf.

If the parent has space for this entry, at all higher levels, we only need to update the CF vectors to reflect the addition of Ent.

Otherwise, we may have to split the parent as well, and so on up to the root.

Merging Refinement: In the presence of skewed data input order, split can affect the clustering quality, and also reduce space utilization. A simple additional merging often helps ameliorate these problems: suppose the propagation of one split stops at some nonleaf node Nj, i.e., Nj can accommodate the additional entry resulting from the split.

Scan Nj to find the two closest entries.

If they are not the pair corresponding to the split, merge them.

If there are more entries than one page can hold, split it again.

During the resplitting, one of the seeds attracts enough merged entries, the other receives the rest entries.

Data

Phase 1:Load into memory by building a CF tree

Initial CF Tree

Phase 2(Optional):Condense into desirable range by building a smaller CF tree

Phase 4(optional and offline):Clustering refinement

Phase 3:Global cluster

SmallerCF Tree Good Cluster

Better Cluster

Figure:Flowchart of Birch Algorithm

BIRCH Clustering Algorithm Phase 2,3 & 4

Phase 2: Condense into desirable range by building a smaller CF tree

To meet the need of the input size range of Phase 3

Phase 3: Global clustering

To solve the problem 1

Approach: re-cluster all subclusters by using the existing global or semi-global clustering algorithms

Phase 4: Global clustering

To solve the problem 2

Approach: use the centroids of the cluster produced by phase 3 as seeds, and redistributes the data points to its closest seed to obtain a set of new clusters. This can use existing algorithm

BIRCH Clustering Phase1 Threshold Value Heuristic approach to increase the threshold

Try to choose new threshold value so that the number of data points that will be scanned under the new threshold value can double .

Approach 1: find the most crowded leaf node and the closest two entries on the leaf can be merged under new threshold.

Approach 2: Assuming that the volume occupied by the leaf clusters grows linearly with data points. a series of value pair: number of data point and volume new volume (a new data point, using least squares linear regression) new threshold

Using some heuristic methods to adjust the above two thresholds and choose one.

BIRCH Clustering Phase1 Delay-Split option

When we run out of memory. There may be more data points that can fit in the current CF tree. We can continue to read data point and write those data points that require to split a node to disk until the disk space is run out. The advantage of this approach is that more data points can fit in the tree before we have to rebuild.

Performance Studies

Complexity Analysis

Experiment with Synthetic Datasets

Performance Comparisons of BIRCH and CLARANS with Synthetic Datasets

Experiment with Real Datasets

Association Algorithm:

MOST FREQUENT PATTERN MINING (MFPM) ALGORITHM

Association rule mining is one of the mostimportant and well defines technique for frequent pattern,associations in a dataset. Association rules [7] are widelyused in various areas such as market analysis, inventory[4] control, and stock data. Apriori algorithm for strongassociation amog the patterns is highly recommended. Inthis work we proposed a new algorithm MFP thatefficiently generates frequent patterns and strongassociation between them Let we have set X of N items ina Dataset having set Y of attributes. This algorithm countsmaximum of each attribute values for each item in thedataset.

The algorithm is as follows

Input: Datasets (DS)

Output:Matrix Most Frequent Pattern (MFP): MFP (DS)

Begin

For each item Xi in DS

for each attribute

count occurrences for Xi C=Count (Xi)

Find attribute name of C having maximum count Mi=Attribute (Ci)

Next [End of inner loop]

Find Most Frequent Pattern

i. MFP=Combine (Mi) Next [End of outer loop

APRIORI ALGORITHM

The candidate-gen function takes Fk-1 and returns a superset (called the candidates) of the set of all frequent k-itemsets. It has two steps

join step: Generate all possible candidate itemsetsCk of length k

prune step: Remove those candidates in Ck

that cannot be frequent.

Candidate gen function

Function candidate-gen(Fk-1)

CkÂ¬Ã†;

forallf1, f2 ÃŽFk-1

withf1 = {i1, , ik-2, ik-1}

andf2 = {i1, , ik-2, ik-1} andik-1 <ik-1 do

cÂ¬ {i1, , ik-1, ik-1}; // join f1 and f2 CkÂ¬CkÃˆ {c};

foreach (k-1)-subset s of cdo if(sÃFk-1) then

deletec from Ck; // prune

returnCk;

EXPERIMENTS AND RESULTS

The data set we used includes data objects each one with seven attributes. Basing on one of the attributes the clustering technique is applied. The attribute for clustering

is decided accordingly. In our application of stock data [7], clustering is performed based on the attribute of quantity sold. The sample data is shown below.

TABLE I. DATA SET

We apply the clustering technique for the initial grouping of the whole data it then give three clusters of DS, SM and FM stock. So the process has two phases as given below

The evaluation of K means, PAM and BIRCH is done and the execution time is tabulated

Interaction Effects Study

The interaction effects study gives us the interaction between Execution time and the iterations. We take the interaction plot between the execution time and iterations. The following can be observed.

When K-means is considered there is a increase in the execution time with respect to the iterations

When K-Medoids [15] is compared to k means there is decrease in the execution time which may improve the overall computational efficiency.

When Birch is compared to these algorithms there has been much improvement in the execution time.

Based on these effects the results are specified in a tabular form showing the iterations and the execution time in milliseconds for clustering algorithms k-means, PAM and BIRCH in combination with MFP.A graph is then plotted basing on these tabulated values.

TABLE II. RESULTS OF ALGORITHMS

Iterations

MFP with K-

Means

MFP with

PAM

MFP with

BIRCH

1

235

155

140

2

368

288

250

3

503

431

388

4

631

576

413

5

777

707

543

6

816

764

567

7

950

890

607

8

1094

937

630

CONCLUSION AND FUTURE WORK

By comparing K-means with MFP, PAM with MFP, Birch with MFP, K-means with Apriori, PAM with Apriori, Birch with Apriori where K-means, PAM and Birch are Clustering algorithms while Apriori and MFP are Association rule mining algorithms. We will achieve that which clustering algorithm is best and time efficient and which association algorithm is best and time efficient. Here the comparison parameter are – 1)Number Of Iteration 2)Size of dataset

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