ART2 Clustering of Multiple Web Objects for Qualitative Web Prefetching

Figures & data

Figure 1. Architecture of ART2 network.

Figure 2. Proposed system.

Table 1. Representation of sample feature vectors.

Access Counts/URL’s	URL1	URL2	URL3	URL4	URL5	URL6	URL7
Original URL Access Counts	16	15	14	10	9	8	17
Min-Max Normalized Values	0.8889	0.7778	0.6667	0.2222	0.1111	0.000	1.0000

Figure 3. Cluster formation through ART2 network.

Figure 4. Cluster formation through ART1 network.

Figure 5. Plot of the top-down weights vs. clusters using ART2.

Figure 6. Plot of the top-down weights vs. clusters using ART1.

Figure 7. Plot of the optimal number of clusters by various algorithms.

Table 2. Training 3: for ρ = 0.9 and number of epochs = 100.

Training (%)	Testing(%)	Hit rate (%)		Number of clusters formed by ART2	Number of clusters formed by ART1
		ART 2	ART 1
10	90	88.9264	86.1170	45	50
20	80	88.9242	86.9399	43	50
30	70	90.5663	86.0123	43	50
40	60	93.5233	89.7321	41	49
50	50	95.1299	89.7411	41	49
60	40	95.1211	90.4808	40	49
70	30	97.1476	93.9944	40	49
80	20	98.1983	95.8529	40	49
90	10	99.9912	96.9790	39	48

Table 3. Training 1: for ρ = 0.4 and number of epochs = 100.

Training (%)	Testing (%)	Hit rate (%)		Number of clusters formed by ART2	Number of clusters formed by ART1
		ART 2	ART 1
10	90	87.5290	86.0001	40	48
20	80	88.9067	86.0323	40	48
30	70	89.3434	86.0135	39	47
40	60	94.6520	89.7217	37	45
50	50	94.6845	89.3402	35	45
60	40	96.2322	90.8008	32	41
70	30	98.1769	93.8590	30	41
80	20	98.1983	94.7247	28	38
90	10	98.0922	96.8719	28	38

Table 4. Training 2: for ρ = 0.7 and number of epochs = 100.

Training (%)	Testing (%)	Hit rate (%)		Number of clusters formed by ART2	Number of clusters formed by ART1
		ART 2	ART 1
10	90	87.2960	85.1170	35	45
20	80	88.9271	85.9399	32	45
30	70	90.7163	87.0123	32	43
40	60	93.5209	90.7321	31	43
50	50	94.1177	92.7411	30	43
60	40	95.2012	93.8078	30	40
70	30	97.739	93.9901	28	40
80	20	98.0023	93.5297	25	39
90	10	98.1293	95.7900	20	38

Figure 8. Plot of the hit rate vs. vigilance value.

Figure 9. Plot of number of clusters vs. vigilance value.

Figure 10. Plot of the hit rate vs. cache size.

Figure 11. Plot of the number of requests vs. performance measures.

Table

Symbols	Description
a, b	Fixed weights in F1 layer
c	Fixed weight to test for reset
d	Winning unit of F2
e	Parameter to prevent division by zero
θ	Noise suppression parameter
α	Learning rate
ρ	Vigilance parameter
n	Number of URLs in ART2
m	Number of clients in ART2
t[n,m]	An array of top-down weights
b[m,n]	An array of bottom-up weights
s[i,j]	input pattern of jth URL for ith client (1D array of URLs)
w[m],x[m],u[m],	A 1_D array of nodes/units in F1 layer
v[m],p[m],q[m]

Table